>> Narrator: Alumni. (upbeat music) >> Announcer: Live from Stanford University in Palo Alto, California, it's theCUBE. Covering Women in Data Science Conference 2018. Brought to you by Stanford. >> Welcome back to theCUBE, we are live at Stanford University for the third annual Women in Data Science Conference, WiDS. I'm Lisa Martin, very honored to be joined by one of the co-founders of this incredible WiDS movement and phenomenon, Dr. Margot Gerritsen. Welcome to theCUBE! >> It's great to be here, thanks so much for being at our conference. >> Oh, likewise. You were the senior associate dean and director of the Institute for Computational Mathematics and Engineering at Stanford. >> Gerritsen: That's right, yep. >> Wow, that's a mouthful and I'm glad I could actually pronounce that. So you have been, well, I would love to give our audience a sense of the history of WiDS, which is very short. You've been on this incredible growth and scale trajectory. But you've been in this field of computational science for what, 30, over 30 years? >> Yeah, probably since I was 16, so that was 35 years ago. >> Yeah, and you were used to being one of few, or if not the only woman >> That's right. >> In a meeting, in a room. You were okay with that but you realized, you know what? There are probably women who are not comfortable with this and it's probably going to be a barrier. Tell us about the conception of WiDS that you and your co-founders had. >> So, May, 2015, Esteban from Walmart Labs, now at Facebook, and Karen Matthys, who's still very active, you know, one of the organizers of the conference, and I were having coffee at a cafe in Stanford and we were lamenting the fact that at another data science conference that we had been to had only had male speakers. And so we connected with the organizers and asked them why? Did you notice? Because very often people are not even aware, it's just such the norm to only have male speakers, >> Right, right. >> That people don't even notice. And so we asked why is that? And they said, "Well, you know we really tried to find "speakers but we couldn't find any." And that really was, for me, the last straw. I've been in so many of these situations and I thought, you know, we're going to show them. So we joke sometimes, a little bit, we say it's sort of a revenge conference. (laughs) We said, let's show them we can get some really outstanding women, and in fact only women. And that's how it started. Now we were sitting at this coffee shop and I said, "Let's do a conference." And they said, "Well, that would be great, next year." And I said, "No, this year. "Let's just do it. "Let's do it in November." We had six months to put it together. It was just a local conference here. We got outstanding speakers, which were really great. Mostly from the area. And then we started live-streaming because we thought it would be fun to do. And to our big surprise, we had 6,000 people on the livestream just without really advertising. That made us realize, in November 2015, my goodness, we're onto something. And we had such amazing responses. We wanted to then scale up the conference and then you can hire a fantastic conference center in San Francisco and get 10,000 people in like they do, for example, at Grace Hopper. But we thought, why not use online technology and scale it up virtually and make this a global event using the livestream, that we will then provide to people, and asking for regional events, local events to be set up all around the world. And we created this ambassador program, that is now in its second year. the first year the responses were actually overwhelming to us already then. We got 75 ambassadors who set up 75 events around the world >> In about 40 countries. >> This was last year, 2017? >> Yeah, almost exactly 13 months ago, and then this year now we have over 200 ambassadors. We have 177 events in 155 cities in 53 countries. >> That's incredible. >> So we're on every continent apart from Antarctica but we're working on that one. >> Martin: I was going to say, that's probably next year. >> Yeah, that's right. >> The scale, though, that you've achieved in such a short time period, I think, not only speaks to the power, like you said, of using technology and using live-streaming, but also, there is a massive demand. >> Gerritsen: There is a great need, yeah. >> For not only supporting, like from the perspective of the conference, you want to support and inspire and educate data scientists worldwide and support females in the field, but it really, I think, underscores, there is still in 2018, a massive need to start raising more profiles and not just inspiring undergrad females, but also reinvigorating those of us that have been in the STEM field and technology for a while. >> Gerritsen: That's right. >> So, what are some of the things, so, this year, not only are you reaching, hopefully about 100,000 people, you mentioned some of the countries involved today, but you also have a new first this year with the WiDS Datathon. >> That's right. >> Tell us about the WiDS Datathon, what was the idea behind it? You announced some winners today? >> Yeah. Yeah, so with WiDS last year, we really felt that we hit a nerve. Now there is an incredible need for women to see other women perform so well in this field. And, you know, that's why we do it, to inspire. But it's a one-time event, it's once a year. And we started to think about, what are some of the ways that we can make this movement, because it's really become a movement, into something more than just an annual, once-a-year conference? And so, Datathon is a fantastic way to do that. You can engage people for several months before the conference, and you can announce the winner at the conference. It is something that can be done really easily worldwide if it is supported again by the ambassadors, so the local WiDS organizations. So we thought we'd just try. But again, it's one of those things we say, "Oh, let's do it." We, I think, thought about this about six months ago. Finding a good data set is always a challenge but we found a wonderful data set, and we had a great response with 1100, almost 1200 people in the world participating. >> That's incredible. >> Several hundred teams. Yeah, and what we said at the time was, well, let's have the teams be 50% female at least, so that was the requirement, we have a lot of mixed teams. And ultimately, of course, that's what we want. We want 50-50, men-women, have them both at the table, to participate in data science activities, to do data science research, and answer a lot of these data questions that are now driving so many decisions. Now we want everybody around the table. So with this Datathon, it was just a very small event in the sense, and I'm sure next year it will be bigger, but it was a great success now. >> Well, congratulations on that. One of the things I saw you on a Youtube video talking about over the weekend when I was doing some prep was that you wanted this Datathon to be fun, creative, and I think those are two incredibly important ways to describe careers, not just in STEM but in data science, that yes, this can be fun. >> Yep. >> Should be if you're spending so much time every day, right, doing something for a living. But I love the creativity descriptor. Tell us a little bit about the room for interpretation and creativity to start removing some of the bias that is clearly there in data interpretation? >> Oh. (laughs) You're hitting the biggest sore point in data science. And you could even turn it around, you say, because of creativity, we have a problem too. Because you can be very creative in how you interpret the data, and unfortunately, for most of us, whenever we look at news, whenever we look at data or other information given to us, we never see this through an objective lens. We always see this through our own filters. And that, of course, when you're doing data analysis is risky, and it's tricky. 'cause you're often not even aware that you're doing it. So that's one thing, you have this bias coming in just as a data scientist and engineer. Even though we always say we do objective work and we're building neutral software programs, we're not. We're not. Everything that we do in machine learning, data mining, we're looking for patterns that we think may be in the data because we have to program this data. And then even looking at some of the results, the way we visualize them, present them, can really introduce bias as well. And then we don't control the perception of people of this data. So we can present it the way we think is fair, but other people can interpret or use little bits of that data in other ways. So it's an incredibly difficult problem and the more we use data to address and answer critical challenges, the more data is influencing decisions made by politicians, made in industry, made by government, the more important it is that we are at least aware. One of the really interesting things this conference, is that many of the speakers are talking to that. We just had Latanya Sweeney give an outstanding keynote really about this, raising this awareness. We had Daniela Witten saying this, and various other speakers. And in the first year that we had this conference, you would not have heard this. >> Martin: Really? Only two years ago? >> Yeah. So even two years ago, some people were bringing it up, but now it is right at the forefront of almost everybody's thinking. Data ethics, the issue of reproducibility, confirmations bias, now at least people now are aware. And I'm always a great optimist, thinking if people are aware, and they see the need to really work on this, something will happen. But it is incredibly important for the new data scientists that come into the field to really have this awareness, and to have the skill sets to actually work with that. So as a data scientist, one of the reasons why I think it's so fun, you're not just a mathematician or statistician or computer scientist, you are somebody who needs to look at things taking into account ethics, and fairness. You need to understand human behavior. You need to understand the social sciences. And we're seeing that awareness now grow. The new generation of data scientists is picking that up now much more. Educational programs like ours too have embedded these sort of aspects into the education and I think there is a lot of hope for the future. But we're just starting. >> Right. But you hit the nail on the head. You've got to start with that awareness. And it sounds like, another thing that you just described is we often hear, the top skills that a data scientist needs to have is statistical analysis, data mining. But there's also now some of these other skills you just mentioned, maybe more on the softer side, that seem to be, from what we hear on theCUBE, as important, >> Gerritsen: That's right. >> As really that technical training. To be more well-rounded and to also, as you mentioned earlier, to have to the chance to influence every single sector, every single industry, in our world today. >> And it's a pity that they're called softer skills. (laughs) >> It is. >> Because they're very very hard skills to really master. >> A lot of them are probably you're born with it, right? It's innate, certain things that you can't necessarily teach? >> Well, I don't believe that you cannot do this without innate ability. Of course if you have this innate ability it helps a little, but there's a growth mindset of course, in this, and everybody can be taught. And that's what we try to do. Now, it may take a little bit of time, but you have to confront this and you have to give the people the skills and really integrate this in your education, integrate this at companies. Company culture plays a big role. >> Absolutely. >> This is one of the reasons why we want way more diversity in these companies, right. It's not just to have people in decision-making teams that are more diverse, but the whole culture of the company needs to change so that these sort of skills, communication, empathy, big one, communication skills, presentation skills, visualization skills, negotiation skills, that they really are developed everywhere, in the companies, at the universities. >> Absolutely. We speak with some companies, and some today, even, on theCUBE, where they really talk about how they're shifting, and SAP is one of them, their corporate culture to say we've got a goal by 2020 to have 30% of our workforce be female. You've got some great partners, you mentioned Walmart Labs, how challenging was it to go to some of these companies here in Silicon Valley and beyond and say, hey we have this idea for a conference, we want to do this in six months so strap on your seatbelts, what were those conversations like to get some of those partners onboard? >> We wouldn't have been able to do it in six months if the response had not been fantastic right from the get-go. I think we started the conference just at the right time. There was a lot of talk about diversity. Several of the companies were starting really big diversity initiatives. Intel is one of them, SAP is another one of them. We were connected with these companies. Walmart Labs, for example, one of the founders of the company was from Walmart Labs. And so when we said, look, we want to put this together, they said great. This is a fantastic venue for us also. You see this with some of these companies, they don't just come and give us money for this conference. They build their own WiDS events around the world. Like SAP built 30 WiDS events around the world. So they're very active everywhere. They see the need, of course, too. They do this because they really believe that a changed culture is for the best of everybody. But they also believe that because they need the women. There is a great shortage of really excellent data scientists right now, so why not look at 50% of your population? >> Martin: Exactly. >> You know, there's fantastic talent in that pool and they want to track that also. So I think that within the companies, there is more awareness, there is an economic need to do so, a real need, if they want to grow, they need those people. There is an awareness that for their future, the long term benefit of the company, they need this diversity in opinions, they need the diversity in the questions that are being asked, and the way that the companies look at the data. And so, I think we're at a golden age for that now. Now am I a little bit frustrated that it's 2018 and we're doing this? Yes. When I was a student 30 some years ago, I was one of the very few women, and I thought, by the time I'm old, and now I'm old, you know, as far as my 18-year-old self, right, I mean in your 50s, you're old. I thought everything would be better. And we certainly would be at critical mass, which is 30% or higher, and it's actually gone down since the 80s, in computer science and in data science and statistics, so it is really very frustrating in that sense that we're really starting again from quite a low level. >> Right. Right. >> But I see much more enthusiasm and now the difference is the economical need. So this is going to be driven by business sense as well as any other sense. >> Well I think you definitely, with WiDS, you are beyond onto something with what you've achieved in such a short time period. So I can only imagine, WiDS 2018 reaching up to 100,000 people over these events, what do you do next year? Where do you go from here? (laughs) >> Well, it's becoming a little bit of a challenge actually to organize and help and support all of these international events, so we're going to be thinking about how to organize ourselves, maybe on every continent. >> Getting to Antarctica in 2019? >> Yeah, but have a little bit more of a local or regional organization, so that's one thing. The main thing that we'd like to do is have even more events during the year. There are some specific needs that we cannot address right now. One need, for example, is for high school students. We have two high school students here today, which is wonderful, and quite a few of them are looking at the live-stream of the conference. But if you want to really reach out to high school students and tell them about this and the sort of skill sets that they should be thinking about developing when they are at university, you have to really do a special event. The same with undergraduate students, graduate students. So there are some markets there, some subgroups of people that we would really like to tailor to. The other thing is a lot of people are very very eager to self-educate, and so what we are going to be putting together, at least that's the plan now, we'll see, if we can make this, is educational tools, and really have a repository of educational tools that people can use to educate themselves and to learn more. We're going to start a podcast series of women, which will be very, very interesting. We'll start this next month, and so every week or every two weeks we'll have a new podcast out there. And then we'll keep the momentum going. But really the idea is to not provide just this one day of inspiration, but to provide throughout the year, >> Sustained inspiration. >> Sustained inspiration and resources. >> Wow, well, congratulations, Margot, to you and your co-founders. This is a movement, and we are very excited for the opportunity to have you on theCUBE as well as some of the speakers and the attendeees from the event today. And we look forward to seeing all the great things that I think are going to come for sure, the rest of this year and beyond. So thank you for giving us some of your time. >> Thank you so much, we're a big fan of theCUBE. >> Oh, we're lucky, thank you, thank you. We want to thank you for watching theCUBE. I'm Lisa Martin, we are live at the third annual Women in Data Science Conference coming to you from Stanford University, #WiDS2018, join the conversation. I'll be back with my next guest after a short break. (upbeat music)

(energetic music) >> Everyone, welcome back to theCUBE's coverage of WiDS 2023. This is the eighth annual Women in Data Science Conference. As you know, WiDS is not just a conference or an event, it's a movement. This is going to include over 100,000 people in the next year WiDS 2023 in 200-plus countries. It is such a powerful movement. If you've had a chance to be part of the Livestream or even be here in person with us at Stanford University, you know what I'm talking about. This is Lisa Martin. I have had the pleasure all day of working with two fantastic graduate students in Stanford's Data Journalism Master's Program. Hannah Freitag has been here. Tracy Zhang, ladies, it's been such a pleasure working with you today. >> Same wise. >> I want to ask you both what are, as we wrap the day, I'm so inspired, I feel like I could go build an airplane. >> Exactly. >> Probably can't. But WiDS is just the inspiration that comes from this event. When you walk in the front door, you can feel it. >> Mm-hmm. >> Tracy, talk a little bit about what some of the things are that you heard today that really inspired you. >> I think one of the keyword that's like in my mind right now is like finding a mentor. >> Yeah. >> And I think, like if I leave this conference if I leave the talks, the conversations with one thing is that I'm very positive that if I want to switch, say someday, from Journalism to being a Data Analyst, to being like in Data Science, I'm sure that there are great role models for me to look up to, and I'm sure there are like mentors who can guide me through the way. So, like that, I feel reassured for some reason. >> It's a good feeling, isn't it? What do you, Hannah, what about you? What's your takeaway so far of the day? >> Yeah, one of my key takeaways is that anything's possible. >> Mm-hmm. >> So, if you have your vision, you have the role model, someone you look up to, and even if you have like a different background, not in Data Science, Data Engineering, or Computer Science but you're like, "Wow, this is really inspiring. I would love to do that." As long as you love it, you're passionate about it, and you are willing to, you know, take this path even though it won't be easy. >> Yeah. >> Then you can achieve it, and as you said, Tracy, it's important to have mentors on the way there. >> Exactly. >> But as long as you speak up, you know, you raise your voice, you ask questions, and you're curious, you can make it. >> Yeah. >> And I think that's one of my key takeaways, and I was just so inspiring to hear like all these women speaking on stage, and also here in our conversations and learning about their, you know, career path and what they learned on their way. >> Yeah, you bring up curiosity, and I think that is such an important skill. >> Mm-hmm. >> You know, you could think of Data Science and think about all the hard skills that you need. >> Mm, like coding. >> But as some of our guests said today, you don't have to be a statistician or an engineer, or a developer to get into this. Data Science applies to every facet of every part of the world. >> Mm-hmm. >> Finances, marketing, retail, manufacturing, healthcare, you name it, Data Science has the power and the potential to unlock massive achievements. >> Exactly. >> It's like we're scratching the surface. >> Yeah. >> But that curiosity, I think, is a great skill to bring to anything that you do. >> Mm-hmm. >> And I think we... For the female leaders that we're on stage, and that we had a chance to talk to on theCUBE today, I think they all probably had that I think as a common denominator. >> Exactly. >> That curious mindset, and also something that I think as hard is the courage to raise your hand. I like this, I'm interested in this. I don't see anybody that looks like me. >> But that doesn't mean I shouldn't do it. >> Exactly. >> Exactly, in addition to the curiosity that all the women, you know, bring to the table is that, in addition to that, being optimistic, and even though we don't see gender equality or like general equality in companies yet, we make progress and we're optimistic about it, and we're not like negative and complaining the whole time. But you know, this positive attitude towards a trend that is going in the right direction, and even though there's still a lot to be done- >> Exactly. >> We're moving it that way. >> Right. >> Being optimistic about this. >> Yeah, exactly, like even if it means that it's hard. Even if it means you need to be your own role model it's still like worth a try. And I think they, like all of the great women speakers, all the female leaders, they all have that in them, like they have the courage to like raise their hand and be like, "I want to do this, and I'm going to make it." And they're role models right now, so- >> Absolutely, they have drive. >> They do. >> Right. They have that ambition to take something that's challenging and complicated, and help abstract end users from that. Like we were talking to Intuit. I use Intuit in my small business for financial management, and she was talking about how they can from a machine learning standpoint, pull all this data off of documents that you upload and make that, abstract that, all that complexity from the end user, make something that's painful taxes. >> Mm-hmm. >> Maybe slightly less painful. It's still painful when you have to go, "Do I have to write you a check again?" >> Yeah. (laughs) >> Okay. >> But talking about just all the different applications of Data Science in the world, I found that to be very inspiring and really eye-opening. >> Definitely. >> I hadn't thought about, you know, we talk about climate change all the time, especially here in California, but I never thought about Data Science as a facilitator of the experts being able to make sense of what's going on historically and in real-time, or the application of Data Science in police violence. We see far too many cases of police violence on the news. It's an epidemic that's a horrible problem. Data Science can be applied to that to help us learn from that, and hopefully, start moving the needle in the right direction. >> Absolutely. >> Exactly. >> And especially like one sentence from Guitry from the very beginnings I still have in my mind is then when she said that arguments, no, that data beats arguments. >> Yes. >> In a conversation that if you be like, okay, I have this data set and it can actually show you this or that, it's much more powerful than just like being, okay, this is my position or opinion on this. And I think in a world where increasing like misinformation, and sometimes, censorship as we heard in one of the talks, it's so important to have like data, reliable data, but also acknowledge, and we talked about it with one of our interviewees that there's spices in data and we also need to be aware of this, and how to, you know, move this forward and use Data Science for social good. >> Mm-hmm. >> Yeah, for social good. >> Yeah, definitely, I think they like data, and the question about, or like the problem-solving part about like the social issues, or like some just questions, they definitely go hand-in-hand. Like either of them standing alone won't be anything that's going to be having an impact, but combining them together, you have a data set that illustrate a point or like solves the problem. I think, yeah, that's definitely like where Data Set Science is headed to, and I'm glad to see all these great women like making their impact and combining those two aspects together. >> It was interesting in the keynote this morning. We were all there when Margot Gerritsen who's one of the founders of WiDS, and Margot's been on the program before and she's a huge supporter of what we do and vice versa. She asked the non-women in the room, "Those who don't identify as women, stand up," and there was a handful of men, and she said, "That's what it's like to be a female in technology." >> Oh, my God. >> And I thought that vision give me goosebumps. >> Powerful. (laughs) >> Very powerful. But she's right, and one of the things I think that thematically another common denominator that I think we heard, I want to get your opinions as well from our conversations today, is the importance of community. >> Mm-hmm. >> You know, I was mentioning this stuff from AnitaB.org that showed that in 2022, the percentage of females and technical roles is 27.6%. It's a little bit of an increase. It's been hovering around 25% for a while. But one of the things that's still a problem is attrition. It doubled last year. >> Right. >> And I was asking some of the guests, and we've all done that today, "How would you advise companies to start moving the needle down on attrition?" >> Mm-hmm. >> And I think the common theme was network, community. >> Exactly. >> It takes a village like this. >> Mm-hmm. >> So you can see what you can be to help start moving that needle and that's, I think, what underscores the value of what WiDS delivers, and what we're able to showcase on theCUBE. >> Yeah, absolutely. >> I think it's very important to like if you're like a woman in tech to be able to know that there's someone for you, that there's a whole community you can rely on, and that like you are, you have the same mindset, you're working towards the same goal. And it's just reassuring and like it feels very nice and warm to have all these women for you. >> Lisa: It's definitely a warm fuzzy, isn't it? >> Yeah, and both the community within the workplace but also outside, like a network of family and friends who support you to- >> Yes. >> To pursue your career goals. I think that was also a common theme we heard that it's, yeah, necessary to both have, you know your community within your company or organization you're working but also outside. >> Definitely, I think that's also like how, why, the reason why we feel like this in like at WiDS, like I think we all feel very positive right now. So, yeah, I think that's like the power of the connection and the community, yeah. >> And the nice thing is this is like I said, WiDS is a movement. >> Yes. >> This is global. >> Mm-hmm. >> We've had some WiDS ambassadors on the program who started WiDS and Tel Aviv, for example, in their small communities. Or in Singapore and Mumbai that are bringing it here and becoming more of a visible part of the community. >> Tracy: Right. >> I loved seeing all the young faces when we walked in the keynote this morning. You know, we come here from a journalistic perspective. You guys are Journalism students. But seeing all the potential in the faces in that room just seeing, and hearing stories, and starting to make tangible connections between Facebook and data, and the end user and the perspectives, and the privacy and the responsibility of AI is all... They're all positive messages that need to be reinforced, and we need to have more platforms like this to be able to not just raise awareness, but sustain it. >> Exactly. >> Right. It's about the long-term, it's about how do we dial down that attrition, what can we do? What can we do? How can we help? >> Mm-hmm. >> Both awareness, but also giving women like a place where they can connect, you know, also outside of conferences. Okay, how do we make this like a long-term thing? So, I think WiDS is a great way to, you know, encourage this connectivity and these women teaming up. >> Yeah, (chuckles) girls help girls. >> Yeah. (laughs) >> It's true. There's a lot of organizations out there, girls who Code, Girls Inc., et cetera, that are all aimed at helping women kind of find their, I think, find their voice. >> Exactly. >> And find that curiosity. >> Yeah. Unlock that somewhere back there. Get some courage- >> Mm-hmm. >> To raise your hand and say, "I think I want to do this," or "I have a question. You explained something and I didn't understand it." Like, that's the advice I would always give to my younger self is never be afraid to raise your hand in a meeting. >> Mm-hmm. >> I guarantee you half the people weren't listening or, and the other half may not have understood what was being talked about. >> Exactly. >> So, raise your hand, there goes Margot Gerritsen, the founder of WiDS, hey, Margot. >> Hi. >> Keep alumni as you know, raise your hand, ask the question, there's no question that's stupid. >> Mm-hmm. >> And I promise you, if you just take that chance once it will open up so many doors, you won't even know which door to go in because there's so many that are opening. >> And if you have a question, there's at least one more person in the room who has the exact same question. >> Exact same question. >> Yeah, we'll definitely keep that in mind as students- >> Well, I'm curious how Data Journalism, what you heard today, Tracy, we'll start with you, and then, Hannah, to you. >> Mm-hmm. How has it influenced how you approach data-driven, and storytelling? Has it inspired you? I imagine it has, or has it given you any new ideas for, as you round out your Master's Program in the next few months? >> I think like one keyword that I found really helpful from like all the conversations today, was problem-solving. >> Yeah. >> Because I think, like we talked a lot about in our program about how to put a face on data sets. How to put a face, put a name on a story that's like coming from like big data, a lot of numbers but you need to like narrow it down to like one person or one anecdote that represents a bigger problem. And I think essentially that's problem-solving. That's like there is a community, there is like say maybe even just one person who has, well, some problem about something, and then we're using data. We're, by giving them a voice, by portraying them in news and like representing them in the media, we're solving this problem somehow. We're at least trying to solve this problem, trying to make some impact. And I think that's like what Data Science is about, is problem-solving, and, yeah, I think I heard a lot from today's conversation, also today's speakers. So, yeah, I think that's like something we should also think about as Journalists when we do pitches or like what kind of problem are we solving? >> I love that. >> Or like kind of what community are we trying to make an impact in? >> Yes. >> Absolutely. Yeah, I think one of the main learnings for me that I want to apply like to my career in Data Journalism is that I don't shy away from complexity because like Data Science is oftentimes very complex. >> Complex. >> And also data, you're using for your stories is complex. >> Mm-hmm. >> So, how can we, on the one hand, reduce complexity in a way that we make it accessible for broader audience? 'Cause, we don't want to be this like tech bubble talking in data jargon, we want to, you know, make it accessible for a broader audience. >> Yeah. >> I think that's like my purpose as a Data Journalist. But at the same time, don't reduce complexity when it's needed, you know, and be open to dive into new topics, and data sets and circling back to this of like raising your hand and asking questions if you don't understand like a certain part. >> Yeah. >> So, that's definitely a main learning from this conference. >> Definitely. >> That like, people are willing to talk to you and explain complex topics, and this will definitely facilitate your work as a Data Journalist. >> Mm-hmm. >> So, that inspired me. >> Well, I can't wait to see where you guys go from here. I've loved co-hosting with you today, thank you. >> Thank you. >> For joining me at our conference. >> Wasn't it fun? >> Thank you. >> It's a great event. It's, we, I think we've all been very inspired and I'm going to leave here probably floating above the ground a few inches, high on the inspiration of what this community can deliver, isn't that great? >> It feels great, I don't know, I just feel great. >> Me too. (laughs) >> So much good energy, positive energy, we love it. >> Yeah, so we want to thank all the organizers of WiDS, Judy Logan, Margot Gerritsen in particular. We also want to thank John Furrier who is here. And if you know Johnny, know he gets FOMO when he is not hosting. But John and Dave Vellante are such great supporters of women in technology, women in technical roles. We wouldn't be here without them. So, shout out to my bosses. Thank you for giving me the keys to theCube at this event. I know it's painful sometimes, but we hope that we brought you great stories all day. We hope we inspired you with the females and the one male that we had on the program today in terms of raise your hand, ask a question, be curious, don't be afraid to pursue what you're interested in. That's my soapbox moment for now. So, for my co-host, I'm Lisa Martin, we want to thank you so much for watching our program today. You can watch all of this on-demand on thecube.net. You'll find write-ups on siliconeangle.com, and, of course, YouTube. Thanks, everyone, stay safe and we'll see you next time. (energetic music)

(upbeat music) >> Hey, everyone. Welcome back to theCUBE's live coverage of Women in Data Science Worldwide Conference, WiDS 2022, live from Stanford Uni&versity. I'm Lisa Martin. I have a guest next here with me. Maggie Wang is here, Autonomy Engineer at Skydio. Maggie, welcome to the program. >> Thanks so much. I'm so happy to be here. >> Excited to talk to you. You are one of the event speakers, but this is your first WiDS. What's your take so far? >> I'm really excited that there's a conference dedicated to getting more women in STEM. I think it's extremely important, and I'm so happy to be here. >> Were you always interested in STEM subjects when you were growing up? >> I think I've always been drawn to STEM, but not only STEM, but I've always been interested in arts, humanities. I'm getting more interested in the science as well. And I think STEM robotics was really my way to express myself and make things move in the real world. >> Nice. So you've got interests, I was reading about you, interests in motion planning, control theory, computer vision and deep learning. Talk to me about those interests. It sounds very fascinating. >> Yeah. So I think what really drew me into robotics was just how interdisciplinary the subject is. So I think a lot goes into creating a robot. So not only is it about actually understanding where you are in the world, it's also about seeing where you are in the world. And it's so interesting, because I feel like humans, you know, we take this all for granted, but it's actually so difficult to do that in an actual robot. So I'm excited about the possibilities of robotics now and in the future. >> Lots of possibilities. And you only graduated from Harvard last May, with a Bachelor's and a Masters? >> Yeah. >> Tell me a little bit about what you studied at Harvard. >> Yeah, so I studied Physics as my undergrad degree. And that was really interesting, because I've always been interested in science. And, actually, part of what got me interested in STEM was just learning about the universe and astrophysics. And that's what gets me excited. And I think I also wanted to supplement that with computer science and building things in the real world. And so that's why I got my Masters in that. And I always knew that I wanted to kind of blend a lot of different disciplines and study them. >> There's so much benefit from blending disciplines, in terms of even the thought diversity alone, which just opens up the opportunities to be almost endless. So you graduated in May. You're now at Skydio. Autonomy Engineer. Talk to me a little bit, first of all, tell me a bit about Skydio as a company, the products, what differentiates it, and then talk to me about what you're doing there specifically. >> So Skydio is a really amazing company. I'm super-fortunate to work there. So what they do is create autonomous drones, and what differentiates them is the autonomy. So in typical drones, it's very difficult to actually make sure that it has full understanding of the environment and obstacle avoidance. So what happens is we fly these drones manually, but we aren't able to harness the full potential of these drones because of lack of autonomy. So what we do is really push into this autonomous sphere, and make sure that we're able to understand the environment. We have deep learning algorithms on the drone, and we have really good planning and controls on the drone as well. So yeah, our company basically makes the most autonomous drones in the market. >> Nice. And tell me about your role specifically. >> Yeah. So as an autonomy engineer, I write algorithms that run on the drone, which is super-exciting. I can create some algorithms and design it, and then also fly it in simulation, and then fly it in the real world. So it's just really amazing to see the things I work with actually come to life. >> And talk to me about how you got involved in WiDS. You were saying it was your first WiDS, and Margot Gerritsen found you on LinkedIn, but what are some of the things that you've heard so far? I mean, I was in one of the panels this morning before we came out to the set, and I loved how they were talking about the importance of mentors and sponsors. Talk to me about some of your mentors along the way. >> Yeah, I had so many great mentors along the way. I definitely would not be here had it not been for them. Starting from my parents, they're immigrants from China, and they inspire me in so many ways. They're very hard-working, and they always encourage me to fail, and just be courageous, and, you know, follow my passions. And I think beyond that, like in high school, I had great mentors. One was an astrophysics professor. >> Wow. >> Yeah. So it was very amazing that I was able to have these opportunities at a young age. And even in high school, I was involved in all girls robotics team. And that really opened my eyes to how technology can be used and why more women should be in STEM. And that, you know, STEM should not be only for males. And it's really important for everyone to be involved. >> It is, for so many reasons. If we look at the data, and the workforce is about 50-50, but the number of women in STEM positions is less than 25%. It's something that's new to the tech industry. What are some of the things that... Do you see that, do you feel that, or are you just really excited to be able to focus on doing the autonomous engineering that you're doing? >> Well, I think that it's kind of easy to try to separate yourself and your identity from your work, but I don't necessarily agree with that. I think you need to, as best as possible, bring yourself to the table and bring your whole identity. And I think part of growing up for me was trying to understand who I was as a woman and also as an Asian American, and try to combine all of my identities into how I bring myself to the workplace. And I think as we become more vulnerable and try to understand ourselves and express ourselves to others, we're able to build more inclusive communities, in STEM and beyond. >> I agree. Very wise words. So you're going to be talking on the career panel today. What are some of the parts of wisdom are you going to leave the audience with this afternoon? >> Well, wisdom. I think everyone should be able to know, and have intuitive understanding of what they actually bring to the table. I think so many times women shy away from bringing themselves and showing up as themselves. And I think it's really important for a woman to understand that they hold a lot of power, that they have a voice that need to be heard. And I think I just want to encourage everyone to be passionate and show up. >> Be passionate and show up. That's great advice. One of the things that was talked about this morning, and we talk about this a lot when we talk about data or data science, is the inherent bias in data. Talk to me about the importance of data in robotics. Is there bias there? How do you navigate around that? >> Yeah, there's definitely bias in robotics. There's definitely a lot of data involved in robotics. So in many cases right now in robotics, we work in specialized fields, so you can see picking robots that will pick in specific factory locations. But if you bring them to other locations, like in your garage or something, and make it clean up, it's really difficult to do so. So I think having a lot of different streams of data and having very diverse sets of data is very important. And also being able to run these in the real world I think is also super-important, and something that Skydio addresses a lot. >> So you talked about Skydio, what you guys do there, and some of the differentiators. What are some of the technical challenges that you face in trying to do what you're doing? >> Well, first of all, Skydio's trying to run everything on board on the drone. So already there's a lot of technical challenges that goes into putting everything in a small form factor and making sure that we trade off between compute and all of these different resources. And yeah, making sure that we utilize all of our resources in the best possible way. So that's definitely one challenge. And making sure that we have these trade-offs, and understand the trade-offs that we make. >> That's a good point. Talk to me about why robotics researchers and industry practitioners, what should be some of the key things that they're focusing on? >> Yeah, so I think right now, as I said, a lot of robotics is in very specialized environments, and what we're trying to do in robotics is try to expand to more complex real world applications. And I think Skydio's at the forefront of this. And trying to get these drones in all different types of locations is very difficult, because you might not have good priors, you might not have good information on your data sources. So I think, yeah, getting good, diverse data and making sure that these robots can work in multiple environments can hopefully help us in the future when we use robots. >> Right. There's got to be so many real world a applications of that. >> Yeah, for sure. >> I imagine. Definitely. So talk to me about being a female in the drone industry. What's that like? Why do you think it's important to have the female voice in mind in the drone industry? >> Well, I think first of all, I think it's kind of sad to see not many women in the drone space, because I think there's a lot of potential for drones to be used for good in all the different areas that women care about. And for instance, like climate change, there's a lot of ways that drones can help in reducing waste in many different ways. Search and rescue, for instance. Those are huge issues, and potential solutions from drones. And I think that if women understand these solutions and understand how drones can be used for good, I think we could get more women in and excited about this. >> And how do you see your role in that, in helping to get more women excited, and maybe even just aware of it as a career opportunity? >> Yeah. So I think sometimes robotics can be a very niche subject, and a lot of people get into it from gaming or other things. But I think if we come to it as a way to solve humanity's greatest problems, I think that's what really inspires me. I think that's what would inspire a lot of young women, is to see that robotics is a way to help others. And also that it may not, if we don't consciously make it so that robotics helps others, and if we don't put our voices into the table, then potentially robotics will do harm. But we need to push it into the right direction. >> Do you feel it's going in the right direction? >> Yes, I think with more conferences like this, like WiDS, I think we're going in the right direction. >> Yeah, this is a great conference. It's one of my favorite shows to host. And you know, it only started back in 2015 as a one-day technical conference. And look at it now. It's a global movement. They found you. You're now part of the community. But there's hundreds of events going on in 60 countries. You have the opportunity there to really grow your network, but also reach a much bigger audience, just based on something like what Margot Gerritsen and the team have done with WiDS. What does that mean to you? >> It means a lot. I think it's so amazing that we're able to spread the word of how technology can be used in many different fields, not just robotics, but in healthcare, in search and rescue, in environmental protection. So just seeing the power that technology can bring, and spreading that to underserved communities, not just in the United States, I love how WiDS is a global community and there's regional chapters everywhere. And I think there should be more of this global collaboration in technology. >> I agree. You know, every company these days is a technology company, or a data company, or both. You think of even your local retailer or grocery store that has to be a technology company. So for women to get involved in technology, there's so many different applications of that. It doesn't have to be just coding, for example. You're doing work with drones. There's so much potential there. I think the more that we can do events like this, and leverage platforms like theCUBE, the more we can get that word out there. >> I agree. >> So you have the career panel. And then you're also doing a tech vision talk. >> Yeah, a tech talk. >> What are some of the things you're going to talk about there? >> Yeah, so I'm going to talk about... So at the career panel, just advice in general to young people who may be as confused and starting off their career, just like I am. And at the tech talk, I'll be talking about some different aspects of Skydio, and a specific use case, which is 3D scanning any physical object and putting that into a digital model. >> Ooh, wow. Tell me a little bit more about that. >> Yeah, so 3D scan is one of our products, and it allows for us to take pictures of anything in the physical world and make sure that we can put it into a digital form. So we can create digital twins into digital form, which is very cool. >> Very cool. So we're talking any type of physical object. >> Mm hm. So if you want to inspect a building, or any crumbling infrastructure, a lot of the times right now we use helicopters, or big snooper trucks, or just things that could be expensive or potentially dangerous. Instead, we can use a drone. So this is just one example of how drones can be used to help save lives, potentially. >> Tremendous amount of opportunity that drones provide. It's very exciting. What are some of the things that you're looking forward to this year? We are very early in calendar year 2022, but what are you excited about as the year progresses? >> Hmm. What am I excited about? I think there's a lot of really interesting drone-related companies, and also a lot of robotics companies in general, a lot of startups, and there's a lot of excitement there. And I think as the robotics community grows and grows, we'll be seeing more robots in real life. And I think that's just extremely exciting to me. >> It is. And you're at the forefront of that. Maggie, it's great to have you on the program. Thank you for sharing what you're doing at Skydio, your history, your past, and what you're going to be encouraging the audience to be able to go and achieve. We appreciate your time. >> Thanks so much. >> All right. From Maggie Wang. I'm Lisa Martin. You're watching theCUBE's coverage of Women in Data Science Worldwide Conference, WiDS 2022. Stick around. I'll be right back with my next guest. (upbeat music)

>> Live from Stanford University, it's theCUBE. Covering Global Women in Data Science Conference. Brought to you by SiliconANGLE Media. >> Welcome back to theCUBE I'm Lisa Martin. We've been live all day at the fourth annual Women in Data Science Conference. I'm with John Furrier, John, this is not just WiDS fourth annual, it's theCUBE's fourth time covering this event. There were, as Margot Gerritsen, Co-Founder stopped by this afternoon and was chatting with me saying, there's over 20,000 people they expect today just to watch the WiDS livestream from Stanford. Another 100,000 engaging in over 150 regional WiDS events, and 50 countries, CUBE's been there since the beginning tell us a little bit about that. >> Well what's exciting about this event is that we've been there from the beginning, present at creation with these folks. Great community, Judy Logan, Karen Matthys, Margot. They're all been great, but the vision from day one has been put together smart people, okay, on a stage, in a room, and bring it, syndicate it out to anyone who's available, meet ups and groups around the world. And if you bet on good content and quality people the community with self-form. And with the Stanford brand behind it, it really was a formula for success from day one. And this is the new model, this is the new reality, where, if you have high quality people in context, the global opportunity around the content and community work well together, and I think they cracked the code. Something that we feel similar at theCUBE is high quality conversations, builds community so content drives community and keep that fly wheel going this is what Women in Data Science have figured out. And I'm sure they have the data behind it, they have the women who can analyze the data. But more importantly is a great community and it's just it's steamrolling forward ahead, it's just great to see. 50 countries, 125 cities, 150 events. And it's just getting started so, we're proud to be part of it, and be part of the creation but continue to broadcast and you know you're doing a great job, and I wish I was interviewing, some of the ladies myself but, >> I know you do >> I get jealous. >> you're always in the background, yes I know you do. You know you talk about fly wheel and Margot Gerritsen we had her on the WiDS broadcast last year, and she said, you know, it's such a short period of time its been three and a half years. That they have generated this incredible momentum and groundswell that every time, when you walk in the door, of the Stanford Arrillaga Alumni Center it's one of my favorite events as you know, you feel this support and this positivity and this movement as soon as you step foot in the door. But Margot said this actually really was an idea that she and her Co-Founders had a few years ago. As almost sort of an anti, a revenge conference. Because they go to so many events, as do we John, where there are so many male, non-female, keynote speakers. And you and theCUBE have long been supporters of women in technology, and the time is now, the momentum is self-generating, this fly wheel is going as you mentioned. >> Well I think one of the things that they did really well was they, not only the revenge on the concept of having women at the event, not being some sort of, you know part of an event, look we have brought women in tech on stage, you know this is all power women right? It's not built for the trend of having women conference there's actual horsepower here, and the payload of the content agenda is second to none. If you look at what they're talking about, it's hardcore computer science, its data analytics, it's all the top concepts that the pros are talking about and it just happens to be all women. Now, you combine that with what they did around openness they created a real open environment around opening up the content and not making it restrictive. So in a way that's, you know, counter intuitive to most events and finally, they created a video model where they livestream it, theCUBE is here, they open up the video format to everybody and they have great people. And I think the counter intuitive ones become the standard because not everyone is doing it. So that's how success is, it's usually the ones you don't see coming that are doing it and they think they did it. >> I agree, you know this is a technical conference and you talked about there's a lot of hardcore data science and technology being discussed today. Some of the interesting things, John, that I really heard thematically across all the guests that I was able to interview today is, is the importance, maybe equal weight, maybe more so some of the other skills, that, besides the hardcore data analysis, statistical analysis, computational engineering and mathematics. But it's skills such as communication, collaboration collaboration was key throughout the day, every person in academia and the industry that we talked to. Empathy, the need to have empathy as you're analyzing data with these diverse perspectives. And one of the things that kind of struck me as interesting, is that some of the training in those other skills, negotiation et cetera, is not really infused yet in a lot of the PhD Programs. When communication is one of the key things that makes WiDS so effective is the communication medium, but also the consistency. >> I think one of the things I'm seeing out of this trend is the humanization of data and if you look at I don't know maybe its because its a women's conference and they have more empathy than men as my wife always says to me. But in seriousness, the big trend right now in machine learning is, is it math or is it cognition? And so if you look at the debate that machine learning concepts, you have two schools of thought. You have the Berkeley School of thought where it's all math all math, and then you have, you know kind of another school of thought where learning machines and unsupervised machine learning kicks in. So, machines have to learn, so, in order to have a humanization side is important and people who use data the best will apply human skills to it. So it's not just machines that are driving it, it's the role of the humans and the machines. This is something we have been talking a lot in theCUBE about and, it's a whole new cutting edge area of science and social science and look at it, fake news and all these things in the mainstream press as you see it playing out everyday, without that contextual analysis and humanization the behavioral data gets lost sometimes. So, again this is all data, data science concepts but without a human application, it kind of falls down. >> And we talked about that today and one of the interesting elements of conversation was, you know with respect to data ethics, there's 2.5 trillion data sets generated everyday, everything that we do as people is traceable there's a lot of potential there. But one of the things that we talked about today was this idea of, almost like a Hippocratic Oath that MDs take, for data scientists to have that accountability, because the human component there is almost one that can't really be controlled yet. And it's gaining traction this idea of this oath for data science. >> Yeah and what's interesting about this conference is that they're doing two things at the same time. If you look at the data oath, if you will, sharing is a big part, if you look at cyber security, we are going to be at the RSA conference this week. You know, people who share data get the best insights because data, contextual data, is relevant. So, if you have data and I'm looking at data but your data could help me figure out my data, data blending together works well. So that's an important concept of data sharing and there's an oath involved, trust, obviously, privacy and monitoring and being a steward of the data. The second thing that's going on at this event is because it's a global event broadcast out of Stanford, they're activating over 50 countries, over 125 cities, they're creating a localization dynamic inside other cities so, they're sharing their data from this event which is the experts on stage, localizing it in these markets, which feeds into the community. So, the concept of sharing is really important to this conference and I think that's one of the highlights I see coming out of this is just that, well, the people are amazing but this concept of data sharing it's one of those big things. >> And something to that they're continuing to do is not just leverage the power of the WiDS brand that they're creating in this one time of year in the March of the year where they are generating so much interest. But Margot talked about this last year, and the idea of developing content to have this sustained inspiration and education and support. They just launched a podcast a few months ago, which is available on iTunes and GooglePlay. And also they had their second annual datathon this year which was looking at palm oil production, plantations rather, because of the huge biodiversity and social impact that these predictive analytics can have, it's such an interesting, diverse, set of complex challenges that they tackle and that they bring more awareness to everyday. >> And Padmasree Warrior talked about her keynote around, former Cisco CTO, and she just ran, car, she's working on a new start up. She was talking about the future of how the trends are, the old internet days, as the population of internet users grew it changed the architecture. Now mobile phones, that's changing the architecture. Now you have a global AI market, that's going to change the architecture of the solutions, and she mentioned at the end, an interesting tidbit, she mentioned Blockchain. And so I think that's something that's going to be kind of interesting in this world is, because there's, you know about data and data science, you have Blockchain it's the data store potentially out there. So, interesting to see as you start getting to these supply chains, managing these supply chains of decentralization, how that's going to impact the WiDS community, I'm curious to see how the team figures that out. >> Well I look forward to being here at the fifth annual next year, and watching and following the momentum that WiDS continues to generate throughout the rest of 2019. For John Furrier, I'm Lisa Martin, thanks so much for watching theCUBE's coverage, of the fourth annual Women in Data Science Conference Bye for now. (upbeat electronic music)

[Announcer] Live from Stanford University, it's The Cube! Covering global women in Data Science Conference. Brought to you by SiliconeANGLE Media. >> Welcome back to The Cube. I'm Lisa Martin, live at Stanford University for the fourth annual Women in Date Science Conference, hashtag WiDS2019. We are here with Kavita Sangwan, the Director of Technical Programs, Artificial Intelligence and Machine Learning at Intuit. Kavita, it's wonderful to have you on the program. >> Thank you, pleasure is all mine. >> So Intuit is a global and visionary sponsor of WiDs, and has been for a couple of years. Talk to us a little bit about Intuit's sponsorship of this WiDs movement. >> Sure, well, Tech Women at Intuit has been important part of our culture. It was founded sometime a couple of years back from our previous CTO Taylor Stansbury. He was the founder and sponsor for it, and it has been getting the continuous support and sponsorship from our current CTO, Marianna Tessel. We highly believe that diversity in inclusion, and diversity in talks, and diversity in employees, is an important aspect for our company because that kind of helps us to deliver awesome product experiences and seamless experiences to our customers. This is our second year at WiDs, and we are proud to be part of this event today. >> It's growing tremendously, you know I mentioned it as a movement, and in three and a half years, this is the fourth annual, as I mentioned, and Margot Gerritsen, one of the co founders, chatted with me a couple hours ago and said they're expecting 20,000 people to be engaging today alone. The live stream at the event here at Stanford, but also the impact that they're making. There's a 150 plus regional events going on around this event in 50 plus countries. >> So it's the... You and I were chatting before we went live that you feel this, this palpable energy when you walk in. Tell me a little bit about your role at Intuit, and how you're able to really kind of grow your career in this organization that really seems to support diversity. >> Sure, I head the Technical Program Management for Intuit Data Science Organization, so it's all about data, data science, AI Machine Learning. We apply and imbed AI Machine Learning across all of our product suites. And also try to apply AI Machine Learning in different other aspects as well. Some of the focus areas where we applying AI Machine Learning is making our products smart, security risk and fraud space, where we are all several steps ahead of the fraudsters. Also, in customer success space, and also within the organization, the products and services our work employees use to make their experiences amazing. I have been with Intuit for almost three years now, and it has been an amazing journey. Intuit is such a... It embraces diversity, and it's because of its diverse, durable, innovative culture, I think Intuit has been in Silicone Valley as a strong force for over 35 years. >> So when we think about Data Science, often we think about the technical skills that a data scientist would need to have, right? It's the computational mathematics and engineering, being able to analyze data, but there's this whole other side that seems to be, based on some of the conversations that we've had, as important but maybe lagging behind, and that is skills on being a team player, being collaborative, communication skills, empathy skills. Tell me about, from your perspective, how do you use those skills in your daily job, and how does Intuit maybe foster some of those communication negotiation skills as equal importance as the actual data itself? >> It's very important for us, as we hire our top talent in our organization to empower and grow that top talent as well. We do that by providing them opportunities to learn from different sessions we host around executive presence, negotiation skills, public speaking skills. In addition to advancing them in their technological space. As you rightly said, it's very important for us to operate in a team setting. You know, a data scientist has to interact with a product manager, and a data engineer, a business person, a legal person, because there is questions about security and privacy. So there are so much interactions happening across functional space, it is very important for us to be a team player, and having the ability to have those conversations in the right way. So, Intuit invests heavily, not just in the technology space to advance women, but also in all the other ancillary spaces, which are equally important to be successful as you advance in your career. >> So, as our viewers understand Intuit, I'm a user of it as well for my business, who understand it to a degree. What do you think would surprise our viewers about how Intuit is applying Data Science? >> So, it's important to know that we operate with a customer's mindset. Everything we do starts with our customers, and it's very important for us to build a culture which reflects the values, and the talent, and the skills of our customers. And that is why I said it's very important for us to have diversity in our teams. Our most opportunistic areas for investment in the AI machine learning is the smart products space where we are heavily investing to make our products intelligent, customize it according to the needs of our customers, and giving them great insights for our customers to save them money, make them do less work, and build more confidence in our product suites. >> Confidence, that word kind of reminds me of another word that we hear used a lot around data, and I'm making it very general, but it's trust. That's something that is critical for any business to establish with the customer, but if we look at how much data we're all generating just as people, and how every company has a trail of us with what we eat, what we buy, what we watch, what we download. Where does trust come into play, if you're really designing these things for the customer in mind, how are you delivering on that promise of trust? >> It's very rightly said, just to add to that sentiment, it has been shared in some articles that we have accumulated so much data in the last two years which is more than what we have accumulated in the last five thousand years of humanity. It is really important to have trust with your customers because we are using their data for their own benefits. Intuit operates with the principle and the mindset that this our customer's data, and we are their stewards. We make sure that we are one of the best stewards for their data, and that's what we reflect in our products, how we serve them, build intelligent products for them, and that's how we start to gain trust from our customers. >> And I imagine being quite transparent in the process. >> That's true, yes. >> So in terms of your career, I was doing some research on you, and I know that you love to give back to the community by being a champion for women in technology, encouraging young girls in STEM towards building that community. Tell me a little bit about your career as we are here at WiDS at Stanford there's a lot of involvement in the student community. Tell me a little about your background and what some of your favorite things are about giving back to the next generation. >> Sure, I actually, when I graduated from engineering, I was one of the four women students out of the, maybe, a class of around 50 students. So I think it struck me right there that there is a disparity in the industry, in the education system, and then in the industry. I felt the same thing in my different companies where I worked, and that always led me to a point that I actually, rather than just being observing this from afar, why can't I be the one who moved the needle on this? That led me to a point where I started collaborating within the companies, started forming teams, and started working with the teams who were already there to move the needle in technical women's space. I think, if I reflect back in my journey, a couple of things that stand out for me is passion for what you do, and I am really passionate about what my goal is and I try to line up my work according to that and that's why this women in tech, something which is close to my heart and I'm passionate about, always comes forward whenever I do something. The second important aspect is, I've always thrown myself into situations which I've never done before. For example we were offline talking about hackathon, which is DevelopHer. I had never done any hackathons before because I was so passionate about doing it, I just threw myself in and I ran that hackathon. And then the third thing is being persistent about what you do. I mean, you can't just do one thing and then drop it and then come back after a few weeks and then do it again. You have to have that consistency of doing it, only then do you start moving the needle. I think when I reflect and look back, these three things stand out for me and that has applied in my own personal career, as well as everything I do in my life. >> How do you give, and the last question, it seems like you sort of have that natural passion, I love this, this is what I want to do, you were persistent with it, how do you advise younger girls who might not have that natural passion to really develop that within themselves? >> I think experiment and explore. When you try to do different things, only then you find out where your passion lies. Just don't be scared of throwing yourself into a situation which you have never dealt before. Always try to find new things and throw yourself in an uncomfortable situation, and try to get out of it. It helps you become super bold, and gives you confidence, and that's the way to find what you're naturally passionate about. >> I like that, I like to say get comfortably uncomfortable. Last question in the last few seconds, I just want you to have the opportunity to tell our viewers where they can go to learn more about Intuit and their Data Science jobs. >> Yes, you can always go to intuit.com, and intuitcareers.com, and learn about the great opportunities we have for Intuit and Data Science. >> Excellent, well Kavita, it's been a pleasure to have you on The Cube this afternoon. Thank you for stopping by, and also for sharing what Intuit is doing to support WiDS. >> Thank you, it was my pleasure, thank you so much. >> We want to thank you for watching The Cube, I'm Lisa Martin live from the WiDS fourth annual WiDS global conference at Stanford. Stick around, I'll be right back with our next guest.

>> Narrator: Live from Stanford University, in Palo Alto California, it's The Cube, Covering Women in Data Science Conference 2018. Brought to you by Stanford. >> Welcome back to The Cube, our continuing coverage of Women in Data Science 2018 continues. I'm Lisa Martin, live from Stanford University, and very excited to be joined by our Co-founder, Co-CEO of SiliconANGLE Media and The Cube, John Furrier. John, what an amazing event, the 3rd Annual WiDS event, the third time The Cube has been here, this event, the energy, the momentum, the excitement, you can feel it. >> I really wanted to interview with you all day, but I wanted to make sure that we had the right women in tech, women in data science. (Lisa laughs) You're an amazing host. I thought it was awesome. What a great powerhouse of women. It's just such an honor for The Cube team and SiliconANGLE to be here. We're listed as a global innovative sponsor on there, so it's like the recognition because they have high integrity. The organizers, Judy, Karen, and Margot, when we first met, when they first started, this "Can you bring The Cube?", of course we will! Because we knew the network effect was big here. They were early on, and they took a great approach. They really nailed the positioning of the event. Use Stanford University as a base, establish a global community, which they have now done. It is so successful, this is the future of events, in my opinion. The way they do it, the way they bring in the content curation here at Stanford, but it's open, it's inclusive, they created a network effect with satellite communities around the world. They've created a VIP network of power women, and it's a shortcut to trust. This is the trusted network of women in data science. It's super exciting. I'm so proud to be part of it in a small way. They get all the credit, but just capturing all the data, the interviews are great data. You've done a great job. The conversations were amazing. The hallway conversations went great. It was just fantastic. >> Yeah it was fantastic, and thank you for handing the keys to The Cube to me for this event. The remarkable thing-- One of the remarkable things to me about this event is that they have, in third year, they're going to reach 100,000 people with this event. There were 177 regional events in the last 24 hours, #WiDS2018, in 53 countries. And we were fortunate to have Margot Gerritsen on a few hours ago, and I said, "You must be pleasantly shocked at this massive trajectory, "but where do go from here?" "Sustaining, maintaining, but also reaching out," she said, "to even younger audiences in high schools "and being able to ignite the bunsen burner, "turn it up a little bit higher." What were some of the hallway conversations that you had? >> Well I think the big thing was is that, first of all, the panels on the conversation of the content was not about women, it was about data science, that happen to be women. >> Yes. So the quality of the conversations, if you close your eyes, you'll be like, "There are some serious pros on here". And they had some side discussions around how to be a woman in tech and data science, and how to use your integrity and reputation, but the content program was top-shelf. I mean, it was fantastic, so that was equalizing. The hallway conversations was global. I heard about global impact, I heard that data science is very mission-driven. And you're seeing a confluence of technology and innovation with technology like data analytics, data science, fueling mission-driven, so standard run your business on analytics, but now run society on analytics. So you're seeing a global framework developing around mission-driven, you'll hear the word "impact" a lot, and it was not just speeds-and-feeds data science, although they're plenty to geek out about, but it was more of a higher level order bit around mission, and society. So this is right around what we're seeing at The Cube around cloud computing, cryptocurrency and blockchain, that you're seeing a democracy being rewritten with technology. Data's the new oil. Oil's power in the new global economy, and you're seeing that in all kinds of decentralized forms of blockchain and cryptocurrency, you're seeing businesses transform with data science, so with that comes a lot of responsibility. So, ethics conversation in the hallway. I felt like I was at a TED talk, meets World Economic Forum, meets Stanford Think Tank, meets practitioner. It was like, really exciting. >> And they had keynotes, which we had a few on some tech tracks, and a career panel. Did you get to listen to the career panel? >> John: The career panel was interesting and I'd love to get your thoughts on some of your interviews that crossover, because it was really more about being proud and high integrity. So the word "democratization" came up, and the conversations in the audience when they had the Q&A was, "Isn't it more about respect?", democratization, not that there's anything wrong with that, but "Isn't it about integrity? "What is the integrity of us as a community, "as women in data science, what is the respect, "integrity, and mission of the role?" Of course democratization is a side effect of good news data, so that was super exciting. And then also, stand up, never give up, never worry about the failure, never worry about getting in a blocker, remove that blocker or as Teresa Carlson at Amazon would say. So there was definitely the woman vibe of "Listen, don't take things lying down. "Have a tough skin. "Take names and kick butt, but be proud." >> That's where a lot of the, when I'd ask some of our guests, "What advice would you give your younger self?" and a lot of them said the same thing, of "Don't be afraid to get out of your comfort zone". My mentor says, "Get comfortably uncomfortable." I think that's pretty hard for a lot-- If I look back at myself 20 years ago I wouldn't have been able to do that. It took a mentor, and just as Maria Klawe has said on The Cube before, the best time to reach and inspire the next generation of females to go into STEM is first semester yoo-nuh-ver-zhen, that's exactly when it happened for me and I didn't plan it, but it took someone to kind of go like Maria said this morning, "Don't be focused "on the things you think you're not good at." So that "failure is not a bad F word" was a theme that we heard a number of times today, and I think, incredibly important. >> And the tweets I tweeted out but it was kind of said differently, I don't know the exact tweet, but I'd kind of paraphrase it by saying Maria from Harvey Mudd said, "Look it, there's plenty of opportunities "in data science, go there." And she compared and contrasted her journey in a male-dominated world with "Look, if you're stuck or you're in a rut, "or you're in somewhere you're uncomfortable with, "from a male perspective or dogma, "or structural system that's not working for you, "just get out of it and go to another venue." Another venue being a growth market. So the message here was there's plenty of opportunities in data science than just data analytics. There's math career paths, there's cryptocurrency, there's blockchain, there's all kinds of different elements. Go where the growth is. If you go where the growth is, you can pioneer and find like-minded individuals. That was a great message I thought, for women, because you're going to find men in those markets that love collaborating with anyone who's smart, and since everyone here's smart, they're saying just go where the growth is. Don't try to go to a stagnant pond where all the dogma and the structural stuff is. That's going to take too long to change. That's my take, but I think that's kind of the message I thought was really, really powerful. And that's the message I'm going to tell my two daughters is "Stand tall, and go after the new territory." >> You can do anything, and that was also a theme of "Don't be afraid to take risks". In any way of life if we don't take risks, we risk losing out on something. That was something we heard a lot. >> John: Let me ask you a question then, because you did the interview. I was jealous, 'cause you know I hate to give up the microphone. >> I know you. (laughs) But I love this event, 'cause it's super awesome. What were some of the highlights for you? Was there a notable interview, was there some sound bites? What were some of the things that you found were inspiring, informational, or notable? >> Oh, all of the above. Everybody. I loved talking with Maria Klawe this morning who, to your point earlier, had to from many generations face the gender bias, and has such a... That her energy alone is so incredibly inspiring. And what she has been able to do as the first female president of Harvey Mudd and the transformation that she's facilitated so far is remarkable. Margot Gerritsen also was a great, inspiring guest for me. She had said, they had this idea three years ago, you were there from the beginning and I said how long was it from concept to first event? Six months. Whoa, strap on your seatbelt. And she said it was almost-- >> And they did it on a limited budget too, by the way. >> Sure. She said it was almost like the revenge conference. Tell us we can't do something, and I heard that theme as well, people saying, "Tell me I can't do something, "and I will prove you wrong in spades." (John laughs) And I think it's an important message. There's still such a gap in diversity. Not just in diversity in gender and ethnicity, there's a thought diversity gap that every industry is missing. That was another kind of common theme, and that was kind of a new term for me, thought diversity. I thought, "Wow, it's incredibly important "to bring in different perspectives." >> And on that point, one of the things I did here in the hallway was a conversation of, this is not just a movement, it's a collection of movements. So it's not one movement, this one is, or women in general, it's a collection of movements, but it's really one movement. So that was interesting, I was kind of like "Hmm", as being a guy I'm like, "Can you women-splain that to me please?" (Lisa and John laugh) >> Yeah, well the momentum that they-- >> What kind of movement is this? (laughing) >> They're achieving. (laughing) I'm sure there'll be a hashtag for that, and speaking of hashtags, I did think it was very cool that today is Monday, #MotivationMonday, this whole day was Motivation Monday to me. And I asked Margot, "Where do you go from here? "You've achieved this in the third year." And she said, "Doing more WiDS events throughout the year, "also starting to deliver resources on demand for folks". Not just females, to your point, this is people in data science, globally, to consume, and then going sort of downstream if you will, or maybe it's upstream, and starting to reach more of that high school age, those girls who might have a desire or interest in something but might think, "I don't think I can do this". >> Well I think one of the things that I'm seeing, and I was glad to be one of the men that stood up, and there's men here, is that men being part of it is super important because these newer markets, like I was just in the Bahamas for a cryptocurrency blockchain event, and there's a lot of younger generations, the whole gender thing to them, they think is nonsense. They should be all equal. So in these new growth areas they're kind of libertarian, but also they're really open and inclusive. It's because of their open-source ethos. So I think for the younger generation in the youth, we can kind of set the table now, and men got to be a part of that. So to be that kind of world where the conversation isn't about women in tech, means that it's all good now, >> Yeah. Right? So the question we've had on The Cube is when we're done with the diversity and inclusion discussion, that means we've accomplished the goal, which is there's no longer a need for that discussion because it's all kind of leveled up. So I mean, a long ways to go for sure, but that's the goal, and I think the younger generations are like, "You old people are like... "We don't view it that way", so we hope that structurally, we have these kinds of conferences where the conversation is not about just women, but the topics, and their gurus at their field. To me, that is the shining light that we want to focus on, because that's also inspirational. Now the stuff that needs to be fixed, is hard conversations, and it's tough but you can do both. And I think that's a message that I hear here. Phenomenal. >> Great to hear though from your perspectives, from what you're hearing with the millennials in the next generation going "Why are you even talking about this?" It would be great if we eventually get there, but some other things that are really key, and some of these companies are WiDS sponsors, Intel and SAP, and what they're doing to achieve, really aggressively, much more gender diversity. We heard Intel talk about it. We heard SAP talk about it today, Walmart Labs as well. And it's still obviously quite a need for it is what it's showing. >> The pay gap is still off. Way too off, yes. >> So that is like, the conversation needs to happen, I'm not trying to minimize that with my other point, but we got to get there. The other thing that's really off, the pay has got to get leveled up and people are working on that. That's great, let's see the progress. Let's look at the data. But the other one that no one's talking about is not only is the pay a problem, the big problem is the titles. So, we've been looking at data amongst a lot of the big companies. Women are getting some pay leveled up, but their titles aren't. So there's still a lot of these little things out there that matter. She's only a VP, and he's an SVP, but she's actually operating at an SVP level, or Senior Director, I mean, this is happening. So much more work to do, but again, the more that they come in with the skills that they got like in here, the networks that are forming, the VIP trust influence networks, it's just phenomenal. I think this is going to really accelerate the peer review, the peer relationships, access to the data, and just the more the merrier. Shine the light on it, turn the sunlight on. >> Exactly, shining a light on the awareness that they're generating, and also that we have a chance to share through The Cube, bringing more light to some of these things that you talked about, the faster, like you said, the more we're going to be able to accelerate making this a non-topic. >> It's our mission. The Cube's mission is to open the content up, get the conversations, document the folks, get them ingested into our network, share our networks open content. The more that that meta data and that knowledge can share digitally, that is the mission that we live for. As you know we love doing it. You did a great job today. >> Lisa: Thank you! It was my pleasure. It's an inspiring event, even just getting prepped for it, and you can hear all the buzz around us that it probably feels-- >> Cocktail party time. It is cocktail party time. Feels pretty darn good. Well John, thanks so much for being our fearless leader and allowing us to come here. And we want to thank you for watching The Cube. We have been live all day at WiDS 2018. Join the conversation. Follow us, @thecube. Join the conversation with #WiDS2018, and please join the conversation and share the videos of some of these fantastic leaders and inspirational folks that we had on the show today. For my co-host, John Furrier, I am Lisa Martin. We'll see ya next time. (electronic music)

>> Announcer: Live from Stanford University in Palo Alto, California, it's theCUBE. Covering Women in Data Science Conference 2018. Brought to you by Stanford. >> Welcome back to theCUBE, we are live at Stanford University for the third annual Women in Data Science Conference, hashtag WiDS2018. Participate in the conversation and you're going to see people at WiDS events in over 177 regions in over 53 countries. This even is aiming to reach about 100,000 people in the next couple of days, which in its third year is remarkable. It's aimed at inspiring and educating data scientists worldwide and of course supporting females in the field. It's also got keynotes, technical vision tracks, and a career panel. And we're excited to welcome back to theCUBE, a cube alumni, Ziya Ma, the Vice President of Software and Services Group and the Director of Big Data Technologies at Intel. Ziya, welcome back to theCube. >> Thanks for having me, Lisa. >> You have been, this is your first time coming to a WiDS event in person and your first year here. You are on the career panel. >> Yes. >> That's pretty cool. Tell us about, you just came from that career panel, tell us about that. What were some of the things that excited you? What are some of the things that surprised you in what you heard at that panel? >> So I think one thing that was really exciting is to see the passion from the audience, so many women excited with data science. And it was the future of what data science can bring. That's the most exciting part. And also, it's very exciting to get connected with so many women professionals. And in terms of, you know, surprise? I think it's a good surprise to see so much advancement in women development in data science. Comparing where we are and where we were two years ago, it's great to see so many woman speakers and leaders talking about their work in the data science space, applying data science to solve real business problems, to solve transportation problems, to solve education, healthcare problems. I think that's the happy surprise, you know, the fast advancement with woman development in this field. >> What were some of the things that you shared, maybe recommendations or advice. You've been in industry for a long time. You've been at Intel for quite a long time. What were some of the things that you felt important to share with the audience, those in-person here at Stanford which is about 400 plus, and those watching the live stream? >> Yeah, you know, Lisa, I provide career coaching actually for many women professionals at Intel and also from the industry. And a lot of them expressed an interest of getting into a data science field. And they ask me, what is the skillset that I need to develop in order to get into this field? I think first, you need to ask yourself, what kind of job you want to get into in this field. You know, there are marketing jobs, there are sales jobs. And even for technical jobs, there are data engineering type of jobs, data visualization, statistician, data science, or AI engineer, machine learning, deep learning engineer. So you have to ask yourself, what kind of job you want to move to and then assess your skillset gap. And work to close that gap. Another advice I give to many woman professionals is that data science appears to have a high bar today. And it may be too significant a jump to move from where you are to a data science field. You may want to move to adjacent field first. And to have a sense of what is it like to work in the data science field and also have more insights with what's going on. And then, to better prepare you for eventually moving into this field. >> Great advice and I think one of the things that jumped out at me was you talked about skillsets. And we often hear a lot of the technical skills, right, that are essential for a data scientist. But there's also softer skills, maybe it's more left brain, right brain, creativity, empathy, communication. Tell me, in your ascension to now the VP level at Intel, what are some of the other skills besides the technical skills that you find as data science as a field grows and infiltrates everything, what are some of those softer skills that you think are really advantageous? >> Great question. I think openness and collaboration are very important soft skills. Because as a data scientist, you need to work with data engineering teams. Because as a data scientist, you extract business insights from the data. But then you cannot work alone. You have to work with the data engineering team who prepares the data infrastructure, stores, and manages the data very efficiently for you to consume. You also have to work with domain experts. Let's say if you are applying data science solutions to solve a real business problem, let's say in a medical field. You need to work with a domain expert from the medical field so that you can tailor your solution towards, you know, addressing some medical problems. So you need to work with that domain expert who knows the business operations and processes in medical field really, really well. So I think that's, you know, collaboration is key. And of course you also want to collaborate maybe with academia and open source community where a lot of real innovations are happening. And you want to leverage the latest technology building blocks so that you can accelerate your data science application or solution advancement. So collaboration and openness are the key. >> Openness is a great one. I'm glad that you brought that up. We had another guest on talking about that earlier. In terms of being open, one, to not expecting, you know, in the scientific method, you go into it with a hypothesis and you think you know what you're going to find or you want to know, I want to find this. And you might not, and being open to going, okay, that's okay, I'm going to course correct. 'Cause failure in this sense is not a bad F word. But also being open to other opinions, other perspectives. That seems to be kind of a theme that we're hearing more about today, it's be willing to be open-minded. >> You know, that's an excellent point, Lisa. You know, I can share one example. When coming from an engineering background, when I first moved into this field, we always had the assumption that when we talk with your customers, they must be looking for something that's high performance. So our initial discussion with our customers centered around Intel product lineup that will give you the highest of performance for deep learning training or for analytics solution. But as we went deeper with the discussion, we realized that's not what customers are looking for in many cases. The fact is that many of them have collected a massive amount of data over the years. They have built analytics applications and you add on top of that. And so as the data representations get more complex, we want to extract more complex insights. That's the time they want to apply deep learning but to the existing application infrastructure. So they're looking for something, let's say deep learning capability, that can be easily integrated into the existing analytics solutions stack, into its existing infrastructure and reuse its existing infrastructure for lower cost of ownership. That's what they are looking for. And high performance is just nice to have. So once we are open-minded to that learning, that totally changed the conversation. Actually, in the last couple of years, we applied that learning and we have collaborated with top cloud service providers like Amazon, Microsoft, Google, and you know, Alibaba and Baidu and a few others to deploy Intel-based deep learning capabilities. Libraries, frameworks, into cloud so that, you know, more businesses and individuals can have access. But again, it's that openness. You truly need to understand what is the problem you are solving before simply just selling a technology. >> Absolutely, and that's one of the best examples of openness that's obviously in this case listening to customers. We think we know the problem that we need to solve and they're telling you, actually, it's not that. It's a nice to have, and you go, whoa, that changes everything! And it also changes, sounds like, the downstream collaboration that Intel knew we need to have in order to drive our business forward and help our customers in every industry do the same thing. >> Exactly, exactly. >> So a couple of things that I'd love to get your perspective on is the culture at Intel. You've been there a long time. What is that culture like in terms of maybe fueling or being a nice opportunity for bringing in this diversity that we so need in every industry? >> Yeah, you know, one thing I want to share, actually, just now during the panel discussion I shared this. I said Intel will be the first high tech company achieving full representation of women and under-represented minorities by the end of this year. >> Wow, by the end of 2018? >> Yes, we pulled in our timeline by two years. Yes, we're well on track for this year. >> Wow. >> To achieve that. And I personally, I like this quote from Brian Krzanich, our CEO, that if we want tech to define the future, we must be representative of that future. So in the last few years now, Intel has put great effort into hiring and retention for diversity. We also have put great effort for inclusion. We want to make sure our employees, every one of them, come to work, bring their full selves for the value add. We also invest in diverse entrepreneurs through Intel capital initiatives. And most importantly, we also partner with academia, universities, to build the pipeline for tech sectors. So we put a lot of effort and we committed about $300 million for closing the gap at the company but also for the high tech sector. So definitely we are very committed to the diversity and inclusion. But that doesn't mean that we only focus on this. And of course, we make sure that our people are bringing the right skillsets and we bring the most qualified people, you know, to do the job. >> On the pipeline front, one of the things I was reading recently is some of the challenges that organizations that are going to, say, college campuses to recruit, some of the missteps they might be taking in terms of if they're trying to bring more females info their organization in STEM roles, don't staff a booth with men, right? Or have the only females that are at a recruitment event be doing, handing out swag, or taking names. Obviously there's important roles to be had everywhere. But that was one of the things that seems to be, well what a simple thing to change. Just flip the model so that the pipeline, to your point, is fueling really what corporations like Intel want to achieve so that that future is really as inclusive and diverse as it should be. The second thing that you mentioned before we went live, from an Intel perspective, is you guys were challenged on the talent acquisition front. And so a few years ago, you started the Women in Big Data Forum to solve that problem. Tell us about that and what have you achieved so far? >> Great question. So you know, this is three or four years ago. And Intel, you know, because I manage the big data engineering organization within Intel, and we are working to hire some diversity talents. So we opened some racks and we look at our candidate pool. There were very few women, actually barely any women in the candidate pool. Again, yes, we always want to hire the most qualified people, but it also does not feel right that when you don't even have any diversity candidates in that pool. Even though we exhausted all possible options, even tried to bring the relevant diversity candidates into the pool. But it's very challenging. So then we reached out to a few industrial partners to see, is Intel the only company that had this problem or you have the same problem? It turned out everyone had the same problem. So yes, people value diversity, they all see the value. But it's very challenging to have a successful recruiting process for diversity. That's the time the few of us gathered together, we said, maybe there is something that we can do to support a stronger woman pipeline for future hiring. And it may take a couple of years, and it may take one year, but unless we start doing something today, we're going to talk about the same problem two years from now. >> Exactly. >> So then with sponsorship from our executive team, Doug Fisher, the Intel software analysis group GM, and also Michael Greene and a few others, we bring the team together, we started to look at networking opportunities, training opportunities. We worked with our industrial partners to offer many free training classes and we also start reaching out to universities to build the pipeline. And especially to motivate the female students to get passionate about big data, about analytics. So as of now, we have more than 2000 members globally for the forum and also we have many chapters. We have chapters along the West Coast in the Bay Area, also East Coast. We also have chapters in Europe and Asia so we're definitely seeing more and more women getting excited with big data and analytics. And also, we have great collaboration with women in data science at Stanford. >> Yeah and it sounds like the momentum, it doesn't sound like the momentum, you can feel it, right? You can feel it online with, I can see a Twitter stream in front of me on this monitor. People are getting involved in droves all across the globe and I said to Margot, I asked her earlier, Margot Gerritsen, one of the founders of WiDS, I said, first of all, you must be pleasantly pretty shocked at how quickly this has ascended. And she said yes, and I said, where do you go from here? And she said, it's really now going to be about getting involved with WiDS more frequently throughout the year. Also, kind of going up a funnel if you will, to high school students and starting to encourage them, excite them, and start that motivation track, if you will, even earlier. And I think that is, in terms to your point about we can't do anything if the pipeline isn't there to support it. One of the things that WiDS is aiming to do, and it sounds like what you're doing as well, similar to Women in Big Data Forum at Intel, is let's start creating a pipeline of women that are educated in the technical side and the software softer skill side that are interested and find their passion so that we can help motivate them, that you can do this. The sky's the limit where data science is concerned. >> Absolutely, absolutely. And it's great to see actually everybody recognize the value of building the pipeline and reaching out beyond the university students. Because have to get more and more girls getting into the science and tech sector. And we have to start from young. And I, yeah, totally agree, I think we really need to build our pipeline and a pipeline for our pipeline. >> Yes, exactly. And also that sort of sustaining momentum as women, you know, go in university and study STEM subjects, get into the field. Obviously retention is a big challenge that the tech industry and STEM fields alike have faced. But that retention, that motivation, and I think organizations like this, just with this, you can feel the passion when you walk into this alumni center at Stanford is really key. We thank you so much for carving out some time to share your insights and your career path and your recommendations on theCUBE and wish you continued success at Intel and with Women in Big Data Forum, which I'm sure we'll see you back at WiDS next year. >> Alright, thank you, thanks Lisa. >> Absolutely, my pleasure. We want to thank you, you have been watching theCUBE live from the Women in Data Science Conference 2018. Hashtag WiDS2018, join the conversation, get involved. I'm Lisa Martin from Stanford. Stick around, I'll be right back with John Furrier to do a wrap of the day. (outro electronic music)

(light electronic music) >> Announcer: Live from Stanford University, in Paolo Alto, California, it's theCUBE. Covering Women in Data Science Conference, 2018. Brought to you by Stanford. >> Welcome back to theCUBE, I'm Lisa Martin. At Stanford University, we're here for the third annual Women in Data Science Conference. #WiDS2018, check it out, be part of the conversation, WiDS is in it's third year, but it's aiming to reach about a hundred thousand people this week alone. There's 177 regional WiDS events in 53 countries. This event here, the main event at Stanford, features key notes, technical vision talks, a career panel, and we're excited to be joined next by Dr. Nathalie Henry Riche. I did that in French. >> Yes. (laughs) Who is a researcher at Microsoft, and Natalie, first of all, welcome to theCUBE. >> Thank you, I'm really thrilled to be here. >> Yeah, you gave a technical vision talk on data visualization, and data driven's story telling. Share with our audience, some of the key messages, that the WiDS audience heard from you earlier today. >> Well, I guess, I gave two main messages. The first one is, that a visualization has two superpowers. >> Lisa: Superpowers? >> Superpowers. >> Tell me girl. The first one is enable you to kind of think about your data in a new way. So, just kind of form hypothesis, and answer questions you didn't even know, you had by your data. So, that's the first one. The second super power, is it's really useful to communicate information, and communicate with a large audience. Visualization helps you, kind of convey your point with data, to back it up. So, that's kind of the short one minute. >> I love that, super super hero, super power. So, WiDS is, as I mentioned at the intro, in its third year, and reaching, it's grown dramatically in such a short period of time. This is your first WiDS, and your first WiDS you are a speaker. What was is that attracted you to WiDS, and you went, yes I want to give some of my time to this, and come down from Seattle? >> Well, so I'm French originally, and my studies I did at engineering school, and it was one of three out of 300 men, right? >> Wow. >> So, I was requested a lot for women in computer science, and engineering. So, I actually really like it. Just meeting all of those people, talking about, you know, trying to bring more women in. Part of the job I'm doing is very creative, so, we're trying to come up with new ideas for visualization. I think having, you know, a wide range of people adds to the mix, and we get so many more exciting ideas. So, I really want to try to have more diverse group of people I can work with, and connect to, and so that's why that attracted me to here. >> Excellent, couple of things that you said I've heard a number of times today. The first one is, what Daniela went and shared, who's also a speaker, that often times, some of the few women in tech, and you mentioned being one of three in 300? Are asked to do a lot of other things. Did you find that, that, okay you're one of the few females, you're articulate, you like speaking, we want you to do all these things. >> Yes, and I say no a lot. (laughs) >> 'Cause I have kids, too. >> That's a skill, too. But yeah, it happens a lot. I think as we go further, it's going to be less and less happening. It's better in the end. So, it's kind of a service, I see it as a service to, you know, my field, and my company. But, at the same time, we'll also get a lot of benefits from it. But that said, I try to cut it down to a manageable level, so two hours flight from Seattle works great. >> Right, right, right. Another thing is that, that you mentioned the creativity. I've heard that a number of times, today from our guest Margot Gerritsen, was on as well. Tell me about your thoughts about being in this data science role, the need for creativity. How does, how it, why is that you might consider it, like a softer skill versus the technical skills. But, how important is that creativity in your job, for example? >> So, my job is really like researcher. Trying to have new ideas, and innovate for Microsoft in particular. So, I'm not really a data scientist, but I build the tools for a data scientist. So, knowing that, creativity is important because you need to kind of think out of the box. What is the next generation of tools that they will need? In turn, they need to think out of the box, kind of get more insight out of the data they're collecting. So, creativity is just like, pervasive to this whole data science thing. Problem solving as well, so you need a lot the left brain, and a lot of the right brain. Kind of both of them together. I think that having different cultures, and different genders, even different age ranges just, you know, makes you think out of the box. That's just what's happening. Discussing with people, I was discussing with someone in cosmology, and I was like, whoa. That brought up a lot of different ideas in me, so, to me, that's really critical part of what I'm doing every day. >> I like that, that kind of aligns to what one of our guests said earlier, and that is the thought diversity. Wow, I've never >> Yes. thought of thought diversity. But, you bring up a good point about it's not just about having women in the field, it's also having diversity, in terms of generations. One of the things that's, I think, pretty unique about WiDS, is it's not just about reaching young women in their first semester at University, for example. Maria Clavijo said that's the ideal time to really inspire. But, it's also reinvigorating women who've been in academia, or industry in stem subjects for a long time. So, you have, we have multiple generations, and to your point, that diversity is important, it's not just about gender, ethnicity. It's also about the diverse perspectives that come from being >> Exactly. from different generations. >> So, it's funny, 'cause I was giving this talk earlier, and it was, one part of it was about time line. When I was researching, you know how people draw time? Well there's, depending some culture, it goes from left to right, but some other culture it's front to back, back to front, right to left. So, we need to be aware of all of that, and it's so much easier to just have the people to converse with right in your office, or next door, to be aware of those. So, that's very important, especially to big companies, like Microsoft, 'cause of, you know, a lot of customers world wide. So, it's very important to just be immersed in that. >> Definitely. So, you have been published, you've got published research, and over 60 articles in leading venues, and human-computer interaction, and information visualization. But, something we chatted about off camera, was very intriguing about visualization and children. Tell me a little bit more about that. >> So, I happen to have two kids, you know, seven and four. I'm passionate about what I'm doing, and I just couldn't keep it out of their hands, right? So, I was just starting, you know, seeing what does my daughter learn at school, like, what does she learn in kindergarten? In fact, in kindergarten, I remember one day, she brought back candies, and I'm like did you get candies from school? She's like no, because we were doing a bar chart. I was like, what? (laughs) So, I was very intrigued in, you know, what do we teach, what do your kids learn? It was fascinating to see that, you know, from an early age, they learn how to do those visualizations. But, they don't really learn how you can lie with them, or you know, to kind of think critically about that. That, you know, maybe you can start your bar chart at two, and you know, you would have less candy, I guess. But, you could, kind of convey the wrong messages. So, I became passionate about this, and decided we need to just improve our teaching about how we can represent data, and how we can also misrepresent it. In the hope that for the next generation to come, they'll be able to look at a chart, and think critically about it. Whether or not it tells the right story with the right data. Kind of beyond, just picture's worth a thousand words, then I'm not going to think about it. >> Yeah. >> This is kind of my personal effort that I try to move myself forward. (chuckles) >> Well, it's so important about having that passion, and I think that's one of things that seems to be inherent about WiDS. Even, you know, yesterday seeing on the Twitter stream, WiDS New Zealand starting in five minutes, and it's been really focused on being so, kind of inclusive. Just sort of naturally, and one of the things that I learned in some of my prep for the show, is the bias that is still there, in data interpretation. You kind of talked about that, and I never really thought about it in that way. But, if a particular group of people is looking at a data set, and thinking it says this, and no other opinions, perspectives, thoughts are able to be incorporated to go, well, maybe it says this. >> Yeah. >> Then we're limiting ourselves in terms of one, the potential that the data has to, you know, help a business, create a new business model. But also, we're limiting our perspectives on making a massive social impact with data. >> Yeah, what I find very interesting is visualization often people think about it at the end of the spectrum. Like, I've collected my data, I analyze it, and now I need to pretty picture to kind of explain what I found. But, the most powerful use of visualization, I think, comes early on. Where you actually just collected your data, and you look at it before you run any statistical test. I did that not long ago with French air traffic data in the Hollands, I put them in, and I saw the little airplanes moving around. Then, what we saw, is one air planes doing loops like this. I was like, what is this going on, right? It was just a drone, doing like tests, right? But, somehow it got looped in into that data set. So, by looking at your data early on, you can detect what's wrong with the data. So then, when you actually run your statistical test, and your analysis, you better reflect what was that data in the first place, you know, what could go wrong there? So, I think inserting visualization early on is also critical to understand what we can really know, and do, and ask, about the data in the first place. >> So, it's kind of like, watching the story unfold, rather than going, we've done all this analysis here's the picture, the story is this. The story is, your sort of, turning it sort of page by page, it sounds like, and watching it, and interpreting it, as it's unfolding. >> Rethinking what you collected in the first place. Is that the right data you collected to answer the question you wanted to ask? Is it a good match or not? Then, rethink that, you know, collect new data, or the missing one, and then go on with your analysis. So, I think to me, it's really a thinking tool. >> It also sounds like another, we talked about the technical skills that had, obviously that a computer scientist, data scientist needs to have. But, there's other skills. Empathy, communication, collaboration. Sounds like also, there needs to be an ideal kind of skill set, it has to include open mindedness. >> Yes. >> Tell me a little bit about some of your experiences there, and not being married to, the data must say this. So, if it doesn't, I'm not going to look anywhere else. Where is open mindedness, in terms of being a critical skill set that needs to come to the field? >> Yeah, I mean we, that's that is totally a re-critical point. Think already, when you're collecting the data, especially as a scientist, when I run experiment, I kind of know what I want to find. Sometimes, you don't find it. You need to kind of embrace it. But, it's hard to have because sometimes, it's like those unconscious bias you have. Like, you're not really necessarily controlling them, and just the way you collected the data in the first place, maybe just, you know, skewed your result. So, it's very important to kind of think ahead of time of all of those bias you could have, and think about all of what could go wrong. Often, the scientific process is actually that trying to think about all of the stuff that could go wrong, and then check whether or not they're wrong. We're trying to infuse that, a little bit over Microsoft as well, kind of, you know, the data that we collect, can we analyze them, can we have teams of people who really think is that the right data? Are we collecting like, world-wide for example? Are we just collecting from the US? So, there's a lot of those, kind of, ethical, and bias, kind of training, and effort to try and remove that. The maximum from our work, and I think that it's across the entire world. I think, with all of this data collection everywhere, we kind of have to do that, very consciously. >> I think two things kind of speak to me that out of what you just said, that we've heard a number of times today. One, that failure, and I don't mean to say that failure is not a bad thing. That's how you, >> That's how you learn, Exactly, >> and grow. Exactly, in many ways it's not a bad F-word, it's this is how everybody that's successful got to wherever they are. But, it's also about embracing, as you said, the word embracing, embracing the fact that you might be bring bias into this, and you have to be okay with maybe this is the wrong data set. If you consider that a failure, consider it, to your point, a growth opportunity. That is one of the themes that we've heard today, and you've, kind of, elaborated on that. The second one is, be okay getting uncomfortable, get out of that comfort zone. Consciously uncomfortable, because when you're able to do that, the possibilities are limitless. >> Yes, and that's what I try to do everyday, 'cause I try to push all of the software that we're doing, and Microsoft is so big, you know, and all of those software are like so there. (laughs) So trying to come up with new ideas, like so many are failures, you know. Oh they won't make money, or they don't actually work when you, you know, for this population. So, most of my work is failure. (laughs) But hey, one success when you know why, and I'm happy about it. >> Exactly, but it's just charting that course to getting to the ah, this is the pot of gold at the end of the rainbow. Well Nathalie, thank you so much for taking some time to talk with us on theCUBE, and sharing your stories. Congratulations on being a speaker, your first WiDS, and we look forward to seeing you back next year. >> Thank you very much. >> We want to thank you for watching theCUBE. I'm Lisa Martin, live from WiDS 2018 at Stanford University. Stick around, I'll be back with my next guest after a short break. (light electronic music)

>> Narrator: Live from Stanford University in Palo Alto, California, it's the CUBE! Covering, Women in Data Science Conference 2018, brought to you by Stanford. >> Welcome back to the CUBE, we are live at Stanford University, we've been here all day at the third annual Women in Data Science Conference, WiDS 2018. This event is remarkable in its growth in scale, in its third year, and that is, in part by the partners and the sponsors that they have been able to glean quite early on. I'm excited to be joined by Vijay Raghavendra, the senior vice president of Merchant Technology and stores as well, from Walmart Labs. Vijay, welcome to the CUBE! >> Thank you, thank you for having me. >> Walmart Labs has been paramount to the success of WiDS, we had Margot Gerritsen on earlier, and I said, "How did you get the likes of a Walmart Labs as a partner?" And, she was telling me that, the coffee-- the coffee shop conversation >> Yeah, the Coupa Cafe! >> That she had with Walmart Labs a few years ago, and said, "Really, partners and sponsors like Walmart have been instrumental in the growth and the scale, of this event." And, we've got the buzz around, so we can hear the people here, but this is the big event at Stanford. There's 177 regional events, 177! In 53 countries. It's incredible. Incredible, the reach. So, tell me a little bit about the... From Walmart Labs perspective, the partnership with WiDS, what is it that really kind of was an "Aha! We've got to do this"? >> Yeah, it's just incredible, seeing all of these women and women data scientists here. It all started with Esteban Arcaute, who used to lead data science at Walmart Labs, and Search, before he moved on to Facebook with Margot. And, Karen in the cafe in Palo Alto, in 2015, I think. And Esteban and I had been talking about how we really expand the leverage of data and data science within Walmart, but more specifically, how we get more women into data science. And, that was really the genesis of that, and, it was really-- credit goes to Esteban, Margot, and Karen for, really, thinking through it, bringing it together, and, here we are. >> Right, I mean bringing it together from that concept, that conversation here at Stanford Cafe to the first event was six months. >> Yeah, from June to November, and, it's just incredible the way they put it together. And, from a Walmart Labs perspective, we were thrilled to be a huge part of it. And, all the way up the leadership chain there was complete support, including my boss Jeremy King, who was all in, and, that really helped. >> Margot was, when we were chatting earlier, she was saying, "It's still sort of surprising," and she said she's been, I think in, in the industry for, 30-plus years, and she said that, she always thought, back in the day, that by the time she was older, this problem would be solved, this gender gap. And she says, "Actually, it's not like it's still stagnant," we're almost behind, in a sense. When I look at the ... women that are here, in Stanford, and those that are participating via those regional events, the livestream that WiDS is doing, as well as their Facebook livestream. You know, the lofty goal and opportunity to reach 100,000 people shows you that there's clearly a demand, there's a need for this. I'd love to get your perspective on data science at Walmart Labs. Tell me a little bit about the team that you're leading, you lead a team of engineers, data scientists, product managers, you guys are driving some of the core capabilities that drive global e-commerce for Walmart. Tell me about, what you see as important for that female perspective, to help influence, not only what Walmart Labs is doing, but technology and industry in general. >> Yeah. So, the team I lead is called Merchant Technology, and my teams are responsible for, almost every aspect of what drives merchandising within Walmart, both on e-commerce and stores. So, within the purview of my teams are everything from the products our customers want, the products we should be carrying either in stores or online, to, the product catalog, to search, to the way the products are actually displayed within a store, to the way we do pricing. All of these are aspects of what my teams are driving. And, data and data science really put me at every single aspect of this. And the reason why we are so excited about women in data science and why getting that perspective is so important, is, we are in the retail business, and our customers are really span the entire spectrum, from, obviously a lot of women shop at Walmart, lot of moms, lot of millennials, and, across the entire spectrum. And, our workforce needs to reflect our customers. That's when you build great products. That's when you build products that you can relate to as a customer, and, to us that is a big part of what is driving, not just the interest in data science, but, really ensuring that we have as diverse and as inclusive a community within Walmart, so we can build products that customers can really relate to. >> Speaking of being relatable, I think that is a key thing here that, a theme that we're hearing from the guests that we're talking to, as well as some of the other conversations is, wanting to inspire the next generation, and helping them understand how data science relates to, every industry. It's very horizontal, but it also, like a tech company, or any company these days is a tech company, really, can transform to a digital business, to compete, to become more profitable. It opens up new business models, right, new opportunities for that. So does data science open up so many, almost infinite opportunities and possibilities on the career front. So that's one of the things that we're hearing, is being able to relate that to the next generation to understand, they don't have to fit in the box. As a data scientist, it sounds like from your team, is quite interdisciplinary, and collaborative. >> And, to us that is really the essence of, or the magic of, how you build great products. For us data science is not a function that is sitting on the side. For us, it is the way we operate as we have engineers, product managers, folks from the business teams, with our data scientists, really working together and collaborating every single day, to build great products. And that's, really how we see this evolving, it's not as a separate function, but, as a function that is really integrated into every single aspect of what we do. >> Right. One of the things that we talked about is, that's thematic for WiDS, is being able to inspire and educate data scientists worldwide, and obviously with the focus of helping females. But it's not just the younger generation. Some of the things that we're also hearing today at WiDS 2018 is, there's also an opportunity within this community to reinvigorate the women that have been in, in STEM and academia and industry for quite a while. Tell me a little bit more about your team and, maybe some of the more veterans and, how do you kind of get that spirit of collaboration so that those that, maybe, have been in, in the industry for a while get inspired and, maybe get that fire relit underneath them. >> That's a great question, because we, on our teams, when you look across all the different teams across different locations, we have a great mix of folks that bring very different, diverse experiences to the table. And, what we've found, especially with the way we are leveraging data, and, how that is invigorating the way we are... How people come to the table, is really almost seeing the art of what is possible. We are able to have, with data, with data science, we are able to do things that, are, really step functions in terms of the speed at which we can do things. Or, the- for example, take something as simple as search, product search, which is one of the, capabilities we own, or my team is responsible for, but, you could build the machine learning ranking, and, relevance and ranking algorithms, but, when you combine it with, for example, a merchant that really fundamentally understands their category, and you combine data science with that, you can accelerate the learning in ways that is not possible. And when folks see that, and see that in operation that really opens up a whole, slew of other ideas and possibilities that they think about. >> And, I couldn't agree more. Looking at sort of the skillset, we talk a lot about, the obvious technical skillset, that a data scientist needs to have, but there's also, the skills of, empathy, of communication, of collaboration. Tell me about your thoughts on, what is an ideal mix, of skills that that data scientist, in this interdisciplinary function, should have. >> Yeah, in fact, I was talking with a few folks over lunch about just this question! To me, some of the technical skills, the grounding in math and analytics, are table stakes. Beyond that, what we look for in data scientists really starts with curiosity. Are they really curious about the problems they're trying to solve? Do they have tenacity? Do they settle for the more obvious answers, or do they really dig into, the root cause, or the root, core of the problems? Do they have the empathy for our customers and for our business partners, because unless you're able to put yourself in those shoes, you're going to be approaching at, maybe, in somewhat of an antiseptic way? And it doesn't really work. And the last, but one of the most important parts is, we look for folks who have a good sense for product and business. Are they able to really get into it, and learn the domain? So for example, if someone's working on pricing, do they really understand pricing, or can they really understand pricing? We don't expect them to know pricing when they come in, but, the aptitude and the attitude is really, really critical, almost as much as the core technical skills, because, in some ways, you can teach the technical skills, but not some of these other skills. >> Right, and that's an interesting point that you bring up, is, what's teachable, and, I won't say what's not, but what might be, maybe not so natural for somebody. One of the things, too, that is happening at WiDS 2018 is the first annual Datathon. And, Margot was sharing this huge number of participants that they had and they set a few ground rules like wanting the teams to be 50% female, but, tell us about the Datathon from your global visionary sponsorship level; what excites you about that in terms of, the participation in the community and the potential of, "Wow, what's next"? >> Yeah... So, it's hugely exciting for us, just seeing the energy that we've seen. And, the way people are approaching different problems, using data to solve very different kinds of problems ... across the spectrum. And for us, that is a big part of what we look for. For us it is really about, not just coming up with a solution, that's in search of a problem, but really looking at real-world problems and looking at it from the perspective of, "Can I bring data, can I bring data science to bear on this problem?", to solve it in ways that, either are not possible, or can accelerate the way we would solve the problems otherwise. And that is a big part of what is exciting. >> Yeah, and the fact that the impact that data science can make to, every element of our lives is, like I said before, it's infinite, the possibilities are infinite. But that impact is something that, I think, how exciting to be able to be in an industry or a field, that is so pervasive and so horizontal, that you can make a really big social impact. One of they other things, too, that Margot said. She mentioned that the Datathon should be fun, and I loved that, and also have an element of creativity. What's that balance of, creativity in data science? Like, what's the mixture, because we can be maybe over-creative, and maybe interpret something that's in a biased way. What is your recommendation on how much creativity can creep into, and influence, positively, data science? >> Yeah, that's a great question, and there's no perfect answer for it. Ultimately, at least my biases towards using data and data science to, solve real problems. And... As opposed to, pure research, so our focus very much is on applied learning, and applied science. And, to me, within that, I do want the data science to be creative, data scientists to be creative, because, by putting too many guardrails, you limit the way in which they would explore the data, that they may come up with insights that, well, we might not see otherwise. And, which is why, I go back to the point I made, when you have data scientists who fundamentally understand a business, and the business problems we are trying to solve, or the business domains, I think they can then come up with very interesting, innovative ways of looking at the data, and the problem, that you might not otherwise. So, I would by no means want to limit their creativity, but I do have a bias towards ensuring that it is focused on problems we are trying to solve. >> Excellent. Well, Vijay, thank you so much for stopping by the CUBE, congratulations on the continued success of the partnership with WiDS and, we're looking forward to seeing what happens the rest of the year, and we'll probably see you next year at WiDS 2019! >> Absolutely, thank you! >> Excellent, we want to thank you, you're watching the CUBE, live from Stanford University, the third annual Women in Data Science Conference. I am Lisa Martin, I'll be right back after a short break with my next guest. (cool techno music)

>> Announcer: Live from Stanford University in Palo Alto, California, it's theCUBE! Covering Women In Data Science Conference 2018. Brought to you by-- >> Coverage of Women in Data Science 2018. I am Lisa Martin. We're at Stanford University. This is where the big in-person event is, but there are more than 177 regional WiDS events going on around the globe today. They are in 53 countries, and they're actually expecting to have about 100,000 people engaged with WiDS 2018. Pretty awesome. I'm joined by one of the speakers for WiDS 2018, Dawn Woodard, the senior data science manager of maps at Uber. Welcome to theCUBE! >> Thank you so much, Lisa. >> It's exciting to have you here. This is your first WiDS, and you are already a speaker. Tell us a little bit about what attracted you to WiDS. What was it that kind of spoke to you as a female leader in data science? >> Well, I tried to do a fair amount of reach-out to women in data science. I really feel like I've been blessed throughout my career with inspiring female mentors, including my mother, for example. Not every woman comes into her career with that kind of mentorship, so I really wanted to reach out and help provide that to some of the younger folks in our community. >> That's fantastic. One of the things that's remarkable about WiDS, one, is the growth and scale that they've achieved reaching such big, broad audiences in such a short time period. But it's also from a thematic perspective, aiming to inspire and to educate data scientists worldwide, and of course, to support females in that. What are some of the, tell us a little bit about your talk is Dynamic Pricing and Matching in Ride Sharing. What are some of the takeaways that the audience watching the livestream and here in person are going to hear from your talk? >> There are two technical takeaways, and then there's one non-technical takeaway. The first technical takeaway is that the matching algorithms that we use are really designed to reduce the amount of time that riders and drivers have to spend waiting in the app. For drivers, that means that we're working to increase the amount of time that they spend on-trip and getting paid. For riders, that means that we're reducing the amount of time that they have to wait to be picked up by a car. That's the first takeaway. The second takeaway is around dynamic pricing, and why it's important in ride-hailing services in particular. It turns out that it's really important in creating a seamless and reliable experience, both for riders and for drivers, so I talk through the technical reasons for that. Interestingly, these technical arguments are based not just on machine learning and statistics, but also on economic analyses and some optimization concepts. The third takeaway is really that data science is this incredibly interdisciplinary environment in which we have economics, statistics, optimization, machine learning, and more. >> It's really, data sciences has the opportunity, or really is, very horizontal. Every sector, every area of our lives is impacted by it. I mean, we think of all of us that use Uber and ride-sharing apps. I think that's one of the neat things that we're hearing from the event and from the speakers like yourself is these demarcated lines of career paths are blurring, or some of 'em are evaporating. And so, I think having the opportunity to talk to the younger generation, showing them how much impact they can make in this field has got to sort of be maybe, I would even guess, invigorating for you, as someone who's been in the tech in both industry and academia for a while. >> Absolutely. I think about data science as being the way that we learn about the world, statistics and data science. So, how do we use data to learn about the world, and how do we use data to improve, to make great products, to make great apps, for example. >> Exactly. Tell me a little bit about your career path. You have your PhD in statistics from Duke University. Tell me about how you got there, and then how you also got into industry. Were you always a STEM fan as a kid, or was it something that you had a passion for early on, or developed over time? >> I was always passionate about math and science. When I was an undergraduate, I did an internship with a defense contractor. That's how I got interested in machine learning in particular. That's where it took off. I decided to get a PhD in statistics from there. Statistics and machine learning are really closely related. And then, continued down that path throughout my academic career, and now my career in tech. >> What are some of the things that you think that prepared you for a being a female leader? Was it those mentors that you mentioned before? Was it the fact that you just had a passion for it and thought, "If I'm one of the only females in the room, I don't care. "This is something that's interesting to me." What were some of those foundational elements that really guided you? >> One is the inspiration of some women in my life, and if we have to be completely honest, I'm a person who, when, the very rare times in my career when somebody has acted like I couldn't hack it or couldn't make it, it always really got me angry. The way that I channeled that was really to turn it around and to say, "No problem. "I'm going to show you that I can go well beyond "anything that you had conceived of." >> You know, I love that you said that, 'cause Margot Gerritsen, one of the founders of WiDS actually said a couple hours ago, a few years ago, when they had this idea, from concept to first conference was six months, and she said she almost thought of it like a revenge conference. Like, "We can do this!" I think it's kind of, when they had this idea in 2015, the fact that even in 2015, there's still not only demand for, but the demand is growing. As we're seeing, the statistics that show a low percentage of women that have degrees in engineering, I want to say 20%, but only 11% of them are actually working in their field. We still have a lot of work to do to ignite the fire in this next generation of prospective leaders in technology. There's still a lot of groundwork to make up there. I think we're hearing that a lot at WiDS. Are you hearing that in your peer groups as well? >> Absolutely. I think one of the things that I've really focused on is mentoring women as leaders and managers within my organization, and I really find that that's an amazing way to reach out, is not just to reach out myself, but also to do that through female leaders in my own organization. For example, I've mentored and managed two women through the transition from individual contributor to manager. Just watching their trajectory afterwards is incredibly inspiring. But then, of course, those female managers bring in additional female contributors, and it grows from there. >> Right. And you have a pretty good, pretty diverse team at Uber. Tell us a little bit about your rise at Uber. One of the things that I saw on your LinkedIn profile, that you achieved pretty quickly in the first three years, or probably less, was that you led the marketplace data science team through a period of transformative growth. You started that team with 10 data scientists, and by the time you transitioned into your next role, there were 49 data scientists, including seven managers. How were you able to come in and make such a big impact so quickly? >> Well, the whole team chipped in in terms of hiring and reaching out. But at the time when I joined Uber, data science was still relatively small. Those 10 people were being asked to do all of the pricing and matching algorithms, all of the data science for Uber Pool, all of the data science for Uber Eats. We just had one person in each of these areas, and those people very quickly stepped up to the plate and said, "Okay, I need help." We worked together to help grow their teams. It's really a collaborative effort involving the whole team. >> The current team that you're managing, what does that look like from a male/female ratio standpoint? >> The current team is more than 50% female at this point, which is something that I'm really proud of. It's definitely not only my achievement. There was a manager who was leading the team just before I switched to leading maps, and that person also helped increase the presence of women in data science for Uber's mapping organization. The first data scientist on maps at Uber was a woman, actually. >> That's fantastic. And you were saying before we went live that there's a good-sized contingent of women data scientists at Uber today that are participating in WiDS up in San Francisco? >> That's right, yes. We're live-streaming it. There's a Women in Data Science organization at Uber, and that organization is sponsoring the internal events for the live stream, not just for my talk, but really, the whole conference. >> That's one of the things that Margot Gerritsen was also saying, that from a timing perspective, they really knew they were on to something pretty quickly, and being able to take advantage of technology, live streaming, they're also doing it on Facebook, gives them that opportunity to reach a bigger audience. It also is, for you and your peers as speakers, gives you an even bigger platform to be able to reach that audience. But one of the things I find interesting about WiDS is it's not just the younger audience. Like Maria Klawe had said in her opening remarks this morning and before, that the optimal time that she's found of reaching women to get them interested in STEM subjects is first year college, first semester of college. I actually had the same exact experience many years ago, and I didn't realize that was a timing that was actually proven to be the most successful. But it's not just young women at that stage of their university career. It's also those who've been in tech, academia, and industry for a while who, we're hearing, are feeling invigorated by events like WiDS. Do you feel the same? Is this something that just sort of turns up that bunsen burner maybe a little bit higher? >> Oh, it's incredibly empowering to be in a room full of such technically powerful women. It's a wonderful opportunity. >> It really is, and I think that reinvigoration is key. Some of the things like, as we look at what you've already achieved at Uber so far, and we're in 2018, what are some of the things that you're looking forward to your team helping to impact for Uber in 2018? >> In 2018, we're looking to magnify the impact of data science within Uber's mapping organization, which is my main focus right now. Maps at Uber does several things. Think of Uber as being a physical logistics platform. We move people and things from point A to point B. Maps, as our physical world, really impacts every aspect of the user experience, both for riders and for drivers. And then, whenever we're making a dispatch decision or a pricing decision, we need to know something about how long it would take this driver to get to this rider, for example, which is really a mapping prediction. We are looking at increasing the presence of data science within the mapping organization, really bringing that perspective to the table, both at the individual contributor level, but really also growing leadership of data science within the mapping organization so that we can help drive the direction of maps at Uber through data-driven insights. >> Data-driven insights, I'm glad that you brought that up. That's something that, as we talk about data science. Data science is helping to make decisions on policy, healthcare, so many different things, you name it. It really seems like these blurred lines of job categories, as businesses use data science, and even Uber, to extend, grow the business, open new business models, so can the next generation leverage data science to just open up this infinite box, if you will, of careers that they can go into and industries they can impact by having this foundation of data science. >> Absolutely. Well, any time we have to make a decision about what direction we go in, right, as a business, for example, as an organization, then doing that starting from data, understanding what is the world really like, what are the opportunities, what are the places in which we as a company are not doing very well, for example, and can make a simple change and get an incredible impact? Those are incredibly powerful insights. What do you think, last question-ish, 'cause we're getting low on time. We talk a lot about, there's the hard skills/soft skills. Soft is kind of a weird word these days to describe that. You know, statistical analysis, data mining. But there's also this, the softer skills, empathy, things like that. How do you find those two sides, maybe it's right brain/left brain, as being essential for people to become well-rounded data scientists? >> The couple of soft skills that I really look for heavily when I'm hiring a data scientist, one is being really focused on impact, as opposed to focused on building a new shiny thing. That's quite a different approach to the world, and if we stay focused on the product that we're creating, that means that we're willing to chip in, even if the work that's being done is not as glamorous, or is not going to get as much attention, or is not as fancy of a model. We can really stay focused on what are some simple approaches that we can use that can really drive the product forward. That kind of impact focus, and also, that great attitude about being willing to chip in on something, even if it's not that fancy or if I'm not going to get in the limelight for doing this. Those are the kinds of soft skills that really are so critical for us. >> Attitude and impact. I've heard impact a number of times today. Dawn, thank you so much for carving out some time to chat with us on theCUBE. We congratulate you on being a speaker at this year's event, and look forward to talking to you next year. >> Thank you, Lisa. >> We want to thank you for watching theCUBE. We are live at Stanford for the third annual Women in Data Science Conference, hashtag #WiDS2018. Get involved in the conversation. It is happening in over 53 countries. After this short break, I will be right back with my next guest. (fast electronic music)

>> Narrator: Live from Stanford University in Palo Alto California, it's theCUBE, covering Women in Data Science Conference 2018. Brought to you by Stanford. >> Back to the cube, our continuing coverage of Women in Data Science 2018 continues. I am Lisa Martin, live from Stanford University. We have had a great array of guests this morning, from speakers, panelists, as well as attendees. This is an incredible one day technical event, and we're very excited to be joined by one of the panelists on the career panel this afternoon, Dr. Jennifer Prendki, the Head of Data Science at Atlassian. Welcome to theCUBE. >> Hi, it's my pleasure to be here. >> It's exciting to have you here. >> So you lead all search and machine learning initiatives at Atlassian, but you were telling me something interesting about your team, tell us about that. >> The interesting thing about my team is even though I'm the Head of Data Science, my team is not 100% data scientists. The belief of the company is that we really wanted to be in charge of our own destiny and be able to deploy our models ourselves and not be depending on other people to make deployment faster. >> Was that one of the interesting kind of culture elements that attracted you last year to Atlassian? >> What is really interesting about Atlassian, it's definitely a company that create products that I would say virtually every single software company in the world is using. They have a very strong software engineering culture, and so last year they decided to embrace data science. I thought it was a very interesting challenge for me to try and infuse a little bit of my passion for data and data-driven est to the company. >> You had quite a fast ramp at Atlassian. You joined last summer, and in less than six months, you grew your team of data scientists and engineers from three people to fifteen, and it gets better, in less than six months, across three locations, Mountain View, San Francisco, and Sydney. What were some of the key things for you that led you to make that impact so quickly? >> I think most data scientists on the world are interested in making an impact, and this is a company that obviously does a lot of impact, and a lot of people talk about this company, and there is obviously a lot of interesting data, and so I think one of the amazing things is that we have a very important role to play, because we are in a position where we have data related to the way people work with each other, collaborate with each other, and this is a very unique data set, so it's usually pretty easy to attract people to Atlassian. >> You mentioned collaboration, and that's certainly an undertone here at WiDS. In its third year, you were here last year as an attendee, now you're here this year as a speaker. They've grown this event dramatically in a couple of years alone. The opportunity to reach, they're expecting, a hundred thousand, to engage. It's a hundred and seventy-seven regional events, Margot Gerritsen gave us that number about an hour ago, in fifty-three countries. What is it about WiDS that attracted you, not only back, this year, but to welcome the opportunity to be on this career panel? >> I'll actually tell you something, so, we talk about diversity, and I think people usually think of diversity as meeting some kind of racial bar, to have, equality between male and female, or specific minorities. I think people tend to forget that the real diversity is diversity of thought, and so I actually found out that the very data science job I actually got, I was actually the only person who had a background in applied math, and everybody else was coming from a background in computer science. I quickly realized that I'm the only person who is really trained to push for, let's validate our models really properly, etc., and so that made realize how important that is to have a lot of diversity. I think WiDS is definitely a place where you see lots of women interested in the same thing, but coming from different perspective, different horizons, at different levels, and this is really something unique in the industry. >> Diversity of thought, I love that. I've not heard that before, I'm going to use that, but I'll give you credit for it. That is one of the things that is so, the more people we speak to, not just at WiDS, but at events like this on theCUBE, you hear, there's still such a need, obviously, the scale of which that WiDS has grown, shows clear demand for, we need more awareness that this diversity is missing, but in the fact that data science is so horizontal, across every industry, and it sort of is blurring the boundaries between rigid job roles, doctor, lawyer, attorney, teacher, whatever. This is quite pervasive and it provides the opportunity for data scientists globally to be able to make massive impact, but also, it still, as Margot Gerritsen was sharing earlier, it still requires what you said is that diversity in thought because having a particular small set of perspectives evaluating data, you think about it from an enterprise perspective, the types of companies that Atlassian deals with, and they are looking to grow and expand and launch new business models, but if the thought diversity is narrow, there's probably a lot of opportunity that is never going to be discovered. One of the things also I found interesting in your background, was that you found yourself sort of at this interesting juxtaposition of being a mentor, and going, wait a minute, this now gives you a great opportunity, but it also comes with some overhead. You've got it from a management perspective. What is that sort of crossroads that you've found yourself reaching and what have you done with that? >> I think it's true of probably every single technical role, but maybe data science more than others, you have to be technical to be part of the story. I think people need to have a leader that they can relate to and I think it's very important that you're still part of this. It's particularly interesting for data science, because data science is a field that moves so quickly. Usually you have people moving on to data science manager positions after being in IC and so if you don't make a conscious effort to remain that technical point of contact person, that people trust and people go to, then, when I think back of the technologies that were trendy when I was still in IC compared to now, it's really important for the managers to be still aware of that, to do a good job as a mentor and as a leader. >> You also said something I think before we went live, that is an important element for the women that WiDS is aiming to inspire and educate, today. Those that are new to the field or thinking about it, as well as those who've been it for a while. There is not just getting there, and going yes I'm interested, this is my passion, I want to have a career in this, it's also having to learn how to be a female leader, and you mentioned from a management perspective, you got to learn, you have to know how to be assertive. Tell us a little bit about the trials and tribulations that you have encountered in that respect. >> That's a very interesting question, because I'm actually very happy to see that nowadays, it's becoming easier and easier for women to step into individual contributor positions, because I think that people realize now that a woman can do just as good a job as men for a defined position, but when you're actually in a leadership position, you have to step into like a thought leadership role. Basically, you sometimes have to be in a meeting where you only have all the male engineers or male data scientists over there and say, you know what, I disagree with you, right? This as a woman becomes a little bit challenging because following the processes that are already in place, I believe that people have realized that it's okay for a woman to do that, but then being the assertive person that goes against the flow and says you are not thinking about it the right way, might sometimes be a problem, because women are not being perceived as creatures that are naturally assertive. It's typical for people, like a Head of Data Science, female data scientists, to be in a situation where they are perceived as being maybe a little bit aggressive or a little bit pushy, and you sometimes fall into this old saying, "he's the boss, she's bossy," kind of thing, and that is a challenge. >> I had someone once tell me a couple years ago, and I'm in tech as well, that I was pushy, and I think this was a language barrier thing, I think he meant to say persistent, but on that front, tell me a little bit more about your team of data scientists and engineers, and the females on your team, how do you help coach them to embrace, it's okay to speak your mind? What's that been like for you? >> I would say I was actually pretty soft-spoken myself. At some point I realized that public speaking actually helped me out there. Somebody at some point told me like, you should go, you're a brilliant, technical like go speak at a conference, and then I realized people are listening to me. You always have a little bit of like imposter syndrome kind of problem as a woman, so it helped me overcome this. Now I'm kind of trained to stimulate the ladies on my group to do the same thing, because that has worked really well for me I think. You have to get outside your comfort zone, and try to, things that help you have the self-confidence for you to get to the level of assertiveness you need to become successful. >> Exactly right, we've had a number of women on the show, today alone, talk about getting outside of your comfort zone, and one of my mentors always says, get comfortably uncomfortable. That's not an easy thing to achieve, but I think you walk in the door at WiDS, and you instantly feel inspired, and empowered. I think a number of the women that we've had on today, already, have talked about having, sort of being charged as a mentor with the responsibility like you just said, of helping those that are following your footsteps, to maybe understand how to have that confidence, and then have that right balance, so that there's professionalism there, there's respect, but it's not just about getting them into the field. It's about teaching them how to, once you're there, how to navigate a career path that is successful. >> That's an interesting thought, because I actually believe that getting comfortable with the uncomfortable is definitely something that data science is about, because you have new technologies, you have new models, you have lateral moves, like I actually was in the advertising industry as a data scientist, before switching to e-commerce and then eventually to the software industry, so I think that people who are trained to be data scientists are like that, and they should also be comfortable with the uncomfortable in their daily lives. >> Yeah, so you were mentioning before we went on that some of the people that you work with are like, it's my hope and dream to be at WiDS next year. What are some of the things that you've heard as we're at the halfway mark of WiDS today, that you're going to go back and share with your team, as well as maybe your friends, other females that are working in STEM fields as well? >> I would say, last year I was here just listening to all the people and whatever. This year, I'm on the panel, so I mean, I'm just like, nothing is impossible, I think. We've proven that over and over again in data science, I mean, who would have thought that ten years ago, we would be at the level of understanding of artificial intelligence and the entire field, right? It's all about waiting and seeing what the future has to bring to you, and we have all these amazing women today, to actually show us that, it's possible to get there, and it's exciting to be here. >> It is possible, and it's exciting. Well, Jennifer, thanks so much for carving out some of your time today to speak with us. We wish you continued success at Atlassian and we look forward to seeing you back at WiDS next year. >> Thank you. >> We want to thank you for watching theCUBE, we're live at Stanford University at the third annual Women in Data Science Conference, hashtag WiDS2018, join the conversation. I'll be right back with my next guest after a short break. (upbeat music)

>> Narrator: Live from Stanford University in Palo Alto, California, it's theCube, covering Women in Data Science conference 2018. Brought to you by Stanford. >> Welcome back to theCube. I'm Lisa Martin and we're live at Stanford University, the third annual Women in Data Science conference, WiDS. This is a great one day technical event with keynote speakers, with technical vision tracks, career panel and some very inspiring leaders. It's also expected to reach over 100,000 people today, which is incredible. So we're very fortunate to be joined by our next guest, Ruth Marinshaw, the CTO for Research Computing at Stanford University. Welcome to theCube, Ruth. >> Thank you. It's an honor to be here. >> It's great to have you here. You've been in this role as CTO for Research Computing at Stanford for nearly six years. >> That's correct. I came here after about 25 years at the University of North Carolina Chapel Hill. >> So tell us a little bit about what you do in terms of the services that you support to the Institute for Computational Mathematics and Engineering. >> So our team and we're about 17 now supports systems, file systems storage, databases, software across the university to support computational and data intensive science. So ICME, being really the home of computational science education at Stanford from a degree perspective, is a close partner with us. We help them with training opportunities. We try to do some collaborative planning, event promotion, sharing of ideas. We have joint office hours where we can provide system support. Margot's graduate students and data scientists can provide algorithmic support to some thousands of users across the campus, about 500 faculty. >> Wow. So this is the third year for WiDS, your third year here. >> Ruth: It is. >> When you spoke with Margot Gerritsen, who's going to be joining us later today, about the idea for WiDS, what were some of your thoughts about that? Did you expect it to make as big of >> Ruth: No. >> an impact? >> No, no people have been talking about this data tsunami and the rise of big data, literally for 10 years, but actually it arrived. This is the world we live in, data everywhere, that data deluge that had been foreseen or promised or feared was really there. And so when Margot had the idea to start WiDS, I actually thought what a nice campus event. There are women all over Stanford, across this disciplines who are engaged in data science and more who should. Stanford, if anything, is known for its interdisciplinary research and data science is one of those fields that really crosses the schools and the disciplines. So I thought, what a great way to bring women together at Stanford. I clearly did not expect that it would turn into this global phenomenon. >> That is exactly. I love that word, it is a phenomenon. It's a movement. They're expecting, there's, I said over a 100,000 participants today, at more than 150 regional events. I think that number will go up. >> Ruth: Yes. >> During the day. And more than 50 countries. >> Ruth: Yes. >> But it shows, even in three years, not only is there a need for this, there's a demand for it. That last year, I think it was upwards of 75,000 people. To make that massive of a jump in one year and global impact, is huge. But it also speaks to some of the things that Margot and her team have said. It may have been comfortable as one of or the only woman at a boardroom table, but maybe there are others that aren't comfortable and how do we help them >> Ruth: Exactly. >> and inspire them and inspire the next generation. >> Exactly. I think it's a really very powerful statement and demonstration of the importance of community and building technical teams in making, as you said, people comfortable and feeling like they're not alone. We see what 100,000 women maybe joining in internationally over this week for these events. That's such a small fraction compared to what the need probably is to what the hunger probably is. And as Margot said, we're a room full of women here today, but we're still such a minority in the industry, in the field. >> Yes. So you mentioned, you've been here at Stanford for over five years, but you were at Chapel Hill before. >> Ruth: Yes. >> Tell me a little bit about your career path in the STEM field. What was your inspiration all those years ago to study this? >> My background is actually computational social sciences. >> Lisa: Oh interesting. >> And so from an undergraduate and graduate perspective and this was the dawn of western civilization, long ago, not quite that long (Lisa laughs) but long ago and even then, I was drawn to programming and data analysis and data sort of discovery. I as a graduate student and then for a career worked at a demographic research center at UNC Chapel Hill, where firsthand you did data science, you did original data collection and data analysis, data manipulation, interpretation. And then parlayed that into more of a technical role, learning more programming languages, computer hardware, software systems and the like. And went on to find that this was really my love, was technology. And it's so exciting to be here at Stanford from that perspective because this is the birthplace of many technologies and again, referencing the interdisciplinary nature of work here, we have some of the best data scientists in the world. We have some of the best statisticians and algorithm developers and social scientists, humanists, who together can really make a difference in solving, using big data, data science, to solve some of the pressing problems. >> The social impact that data science and computer science alone can make with ideally a diverse set of eyes and perspectives looking at it, is infinite. >> Absolutely. And that's one reason I'm super excited today, this third WiDS for one of the keynote speakers, Latanya from Harvard. She's going to be talking, she's from government and sort of political science, but she's going to be talking about data science from the policy perspective and also the privacy perspective. >> Lisa: Oh yes. >> I think that this data science provides such great opportunity, not just to have the traditional STEM fields participating but really to leverage the ethicists and the humanists and the social sciences so we have that diversity of opinions shaping decision making. >> Exactly. And as much as big data and those technologies open up a lot of opportunities for new business models for corporations, I think so does it also in parallel open up new opportunities for career paths and for women in the field all over the world to make a big, big difference. >> Exactly. I think that's another value add for WiDS over it's three years is to expose young women to the range of career paths in which data science can have an impact. It's not just about coding, although that's an important part. As we heard this morning, investment banking, go figure. Right now SAP is talking about the impact on precision medicine and precision healthcare. Last year, we had the National Security Agency here, talking about use of data. We've had geographers. So I think it helps broaden the perspective about where you can take your skills in data science. And also expose you to the full range of skills that's needed to make a good data science team. >> Right. The hard skills, right, the data and statistical analyses, the computational skills, but also the softer skills. >> Ruth: Exactly. >> How do you see that in your career as those two sides, the hard skills, the soft skills coming together to formulate the things that you're doing today? >> Well we have to have a diverse team, so I think the soft skills come into play not just from having women on your team but a diversity of opinions. In all that we do in managing our systems and making decisions about what to do, we do look at data. They may not be data at scale that we see in healthcare or mobile devices or you know, our mobile health, our Fitbit data. But we try to base our decisions on an analysis of data. And purely running an algorithm or applying a formula to something will give you one perspective, but it's only part of the answer. So working as a team to evaluate other alternative methods. There never is just one right way to model something, right. And I think that, having the diversity across the team and pulling in external decision makers as well to help us evaluate the data. We look at the hard science and then we ask about, is this the right thing to do, is this really what the data are telling us. >> So with WiDS being aimed at inspiring and educating data scientists worldwide, we kind of talked a little bit already about inspiring the younger generation who are maybe as Maria Callaway said that the ideal time to inspire young females is first semester of college. But there's also sort of a flip side to that and I think that's reinvigorating. >> Yes. >> That the women who've been in the STEM field or in technology for awhile. What are some of the things that you have found invigorating in your own career about WiDS and the collaboration with other females in the industry? >> I think hearing inspirational speakers like Maria, last here and this year, Diane Greene from Google last year, talk about just the point you made that there's always opportunity, there's always time to learn new things, to start a new career. We don't have to be first year freshmen in college in order to start a career. We're all lifelong learners and to hear women present and to see and meet with people at the breakout sessions and the lunch, whose careers have been shaped by and some cases remade by the opportunity to learn new things and apply those skills in new areas. It's just exciting. Today for this conference, I brought along four or five of my colleagues from IT at Stanford, who are not data scientists. They would not call themselves data scientists, but there are data elements to all of their careers. And watching them in there this morning as they see what people are doing and hear about the possibilities, it's just exciting. It's exciting and it's empowering as well. Again back to that idea of community, you're not in it alone. >> Lisa: Right. >> And to be connected to all of these women across a generation is really, it's just invigorating. >> I love that. It's empowering, it is invigorating. Did you have mentors when you were in your undergraduate >> Ruth: I did. >> days? Were they males, females, both? >> I'd say in undergraduate and graduate school, actually they were more males from an academic perspective. But as a graduate student, I worked in a programming unit and my mentors there were all females and one in particular became then my boss. And she was a lifelong mentor to me. And I found that really important. She believed in women. She believed that programming was not a male field. She did not believe that technology was the domain only of men. And she really was supportive throughout. And I think it's important for young women as well as mid-career women to continue to have mentors to help bounce ideas off of and to help encourage inquiries. >> Definitely, definitely. I'm always surprised every now and then when I'm interviewing females in tech, they'll say I didn't have a mentor. >> Lisa: Oh. >> So I had to become one. But I think you know we think maybe think of mentors in an earlier stage of our careers, but at a later stage we talked about that reinvigoration. Are you finding WiDS as a source of maybe not only for you to have the opportunity to mentor more women but also are you finding more mentors of different generations >> Oh sure. >> as being part of WiDS? >> Absolutely, think of Karen Mathis, not just Margot but Karen, getting to know her. And we go for sort of walks around the campus and bounce ideas of each other. I think it is a community for yes, for all of us. It's not just for the young women and we want to remain engaged in this. The fact that it's global now, I think a new challenge is how do we leverage this international community now. So our opportunities for mentorship and partnership aren't limited to our local WiDS. They're an important group. But how do we connect across those different communities? >> Lisa: Exactly. >> They're international now. >> Exactly. I think I was on Twitter last night and there was the WiDS New Zealand about to go live. >> Yeah, yeah. >> And I just thought, wow it's this great community. But you make a good point that it's reached such scale so quickly. Now it's about how can we learn from women in different industries in other parts of the world. How can they learn from us? To really grow this foundation of collaboration and to a word you said earlier, community. >> It really is amazing though that in three years WiDS has become what it has because if you think about other organizations, special interest groups and the like, often they really are, they're not parochial. But they tend to be local and if they're national, they're not at this scale. >> Right. >> And so again back to it's the right time, it's the right set of organizers. I mean Margot, anything that she touches, she puts it herself completely into it and it's almost always successful. The right people, the right time. And finding ways to harness and encourage enthusiasm in really productive ways. I think it's just been fabulous. >> I agree. Last question for you. Looking back at your career, what advice would you have given young Ruth? >> Oh gosh. That's a really great question. I think to try to connect as much as you can outside your comfort zone. Back to that idea of mentorship. You think when you're an undergraduate, you explore curricula, you take crazy classes, Chinese or, not that that's crazy, but you know if you're a math major and you go take art or something. To really explore not just your academic breadth but also career opportunities and career understanding earlier on that really, oh I want to be a doctor, actually what do you know about being a doctor. I don't want to be a statistician, well why not? So I think to encourage more curiosity outside the classroom in terms of thinking about what is the world about and how can you make a difference. >> I love that, getting out of the comfort zone. One of my mentors says get comfortably uncomfortable and I love that. >> Ruth: That's great, yeah. >> I love that. Well Ruth, thank you so much for joining us on theCube today. It's our pleasure to have you here and we hope you have a great time at the event. We look forward to talking with you next time. >> We'll see you next year. >> Lisa: Excellent. >> Thank you. Buh-bye. >> I'm Lisa Martin. You're watching theCube live from Stanford University at the third annual Women in Data Science conference. #WiDS2018, join the conversation. After this short break, I'll be right back with my next guest. Stick around. (techno music)