>> Live from Stanford University. It's the Cube covering Global Women and Data Science Conference brought to you by Silicon Angle media. >> Welcome back to the Cubes Coverage of the fourth annual Women in Data Science Conference. This global winds event is the fourth annual our fourth year here, covering it for the Cuban Lisa Martin, joined by Gianluca Pecorino, the director on the Stanford Institute for Computational and Mathematical Engineering. Gianluca, it's a pleasure to have you on the program. Thank you. So the Institute for Computational and Mathematical Engineering. I see M e. Tell us a little bit about that and its involvement in wins. >> Yes, so the status has. Bean was funded fifteen years ago at Stanford as a really hard before computation of mathematics at Stanford. The intention was to connect computations and in general, the disciplines around campus towards using computing for evolution, for starting new ideas for pursuing new endeavors. And I think it's being extremely successful over the years in creating a number of different opportunities. Now weeds started four years ago. As you mentioned, it's part of an idea that the prior director advising me, Margo Garretson, had with few others, and I think the position of I see me at the center of campus really helped bring these events sort of across different fields and this different disciplines. And I think, has Bean extremely successful in expanding and creating a new, a completely new movement, a completely new way of off off engaging with with a large, very large community. And I think I seem, has Bean very happy to play this role? And I'm continuing to be excited about the opportunities >> you mentioned expansion and movement to things that jump out. Expansion way mentioned fourth annual on Lee started This Is three and a half years ago knew that twenty fifteen and we were had the pleasure of having Margo Garrett send one of the co founders of Woods on the Cube last year at wigs. And I loved how she actually said. Very cheeky winds really started sort of as a revenge conference for her and the co founders, looking at all of the technology, events and industry events and single a lack of diversity. But in terms of expansion, this there are one hundred fifty plus regional winds events this year in fifty plus countries. They're expecting over one hundred thousand people to engage this expansion. In this movement that you mentioned, it's palpable. Tell us about your Where's the impetus for you to be involved in the woods movement. >> Well, I think my interest in in data science and which particular is because of the role that I seem years in the education at Stanford. We obviously have a very important opportunity toe renew and remodel our curriculum and provide new opportunities for for education off the new generations and clearly starting with with the opportunity off being such an audience and reaching so many different discipline. It's a very different fields. Helps us understand exactly how to put that curriculum together. And so my focus of my interest has been mostly on making sure that I see me alliance with these new directions. And when we establish the institute, computational mathematics didn't really not have data is a very, very critical component, but we are adjusting to that clearly is becoming more and more important. We want to make sure we are ready for it, and we make sure that the students through our curriculum are ready for the world out there. >> So let's talk about this. The students and the curriculum. You've been a professor at Stanford for a very long time before we get into the specifics of today's curriculum. Tell me a little bit about how you have seen that evolve over time as we know that. You know, we're sort of in terms of where the involvement and women and technology and stump field words in the eighties and how that's dropped off. Tell me a little bit about the evolution in that curriculum that you've seen and where the ice Amy is today with that adaptation. >> Yes, certainly. The evolution has bean very quick. In the last few years, we have seen, um in a number of opportunity emerging because of the technology that is out there. The fact that certainly for data science, both the software and the artwork and the technology, the methodology, the algorithms are all in the open so that there is no real barrier into sort of getting started. And I think that helps everybody sort of getting excited about the idea and the opportunity very, very quickly. So we don't really need to goto an extensive curriculum to be ableto ready, solve problems and have an impact. And I think that, perhaps is one one other reason why we are sort of in a level playing field right. Everything is is available to everybody with relatively minor investment at the beginning. And so I think that certainly a difference with respect what the disciplines, where instead, it was much more laborious process to go through before you can actually start having an impact. Suffering every o opportunity, toe change world to toe come, you know, sort of your your vision's sort of impact in the world. So I think that's That's definitely something that the data science and the recent development into the science have created. And so I think, in terms of our role, sort of continuing role in this is tow Pet Shop six. You know, expand the view ofthe data. Science is not just the algorithm, the technology, the statistical learning that you need to accomplish. A student is a new comet into the field, but also is other other elements. And I would say certainly the challenges that we are that are opposed to data. Since they are challenges that have to do with the attics with privacy on DSO, these are clear, clearly difficult to handle because they require knowledge across disciplines the typical air not related to stem in In a traditional sense. But then, on the other hand, I think is the opportunity to be really creative. Data is not analyzing on its own right. He needs the input are sort of help in creating a story. And I think that's that's another element that he makes data science a little bit different. Another stem disciplines intend to be much more ascetic, much more sort of a cold if you like. I think >> that's where the things to you that I find really interesting is if you look at all the statistical and computational skills as you mentioned, that a good data scientist needs to have as we look at some of the challenges with the amount of data being created. So you mentioned privacy, ethics, cybersecurity issues. The create creative element is key for the analysis. Other things, too. That interest me, and I'd love to get your thoughts on how you see this being developed on the curriculum. Helping is is empathy, collaboration, communication skills. Where is that in the curriculum and how important you are? Those other skills to the hard skills >> that that's That's a great question. And I think where is in the curriculum? I think we're lagging behind that. This is one of the opportunities that we have to actually connect to our other places on campus, where instead the education is built much more closely around some of these topics is that you mentioned. So I think you know, again, I the real advantage in the real opportunity we have is that the data science in general reaches out to all these different disciplines in a very, very new way if you like. I think it's it's probably one of the reasons why so attractive toe younger generation is the fact that it's not just the art skills. You do need to have a lot off understanding of the technology, the foundational statistics and mathematics and so on. But it's much more than that. Communication is very important. Teamwork is extremely important. Transparency is very important. There are there are really all these elements that do not really make that they really didn't have a place in some of the more traditional dissidents. And I think that that's definitely a great way off. Sort of refreshing are way off, even considering education and curriculum. >> When you talk to some like the next to the younger generations. Is that one of the things that they find are they pleasantly surprised, knowing that I need to actually be pretty well rounded to me? A successful data scientists? It's how I analyzed the data. How I tell a story, is that something that you still find that excites but surprises this younger generation of well, that >> certainly is a component, very important component of the excitement of the sea. Are there the fact that you can really build the story, tell a story, communicated story and oven, in fact, immediately, quickly, I think is a is something that the newer generation really see it assess a great opportunity and, you know, and it tried to me. So I mean, it has been very difficult for more traditional disciplines to have the same level of impact, partly because the communities tend to be very close, very limited with with a lot of scrutiny. I think what we have in India, the scientists, that is really a lot off you no can do attitude the lot off, Really. You know, creative force that is >> behind, you know, >> basically this movement, but in general data science, I think that >> you write. The impacts is so potent and we've seen it and we're seeing it in every industry across the globe. But it's such an exciting time with Gianluca. We thank you so much for sharing some of your time on the program this morning and look forward to hearing more great things that the ice Amy is helping with prospective women in Stem over the next year. >> Absolutely. Thank you very much. >> My pleasure. We want to thank you. You're watching the Cube live from the fourth annual Women and Data Science Conference here at Stanford University. I'm Lisa Martin. Stick around. My next guest will join me in just a moment.

>>Live from Stanford university on your host. Lisa Martin. My next guest joins me with many I, our corporate vice president at Microsoft, Rick Minnie. It's great to have you on the program. Thank you for having me. Tell me a little bit about your background. So you run Microsoft advertising, engineering organizations. You also manage a multi-billion dollar marketplace globally. Yes. Big responsibilities. >>A little bit >>About you and your role at Microsoft. >>So basically online advertising, you know, funds a lot of the consumer services like search, you know, feeds. And so I run all of the online advertising pieces. And so my team is a combination of machine learning in theory, software engineers, online services. So you think of you think of what needs to happen for running an online advertising ecosystem? That's billions of dollars. I have all these people on my team when I get to work with these fantastic people. So that's my >>Roles. We have a really diverse team. >>Yes. My background itself is in AI. So my PhD was in language modeling and natural language processing. That's how I got into the space. And then I did, you know, machine learning. Then I did some auctions and then I'd, you know, I basically have touched almost all pieces of the puzzle. So from, I appreciate what's required to run a business the size. And so from that perspective, you know, yeah, it is a lot of diverse people, but at the same time, I feel like I know what they do >>Right then interdisciplinary collaboration must be incredibly important and >>Powerful. It is. I mean, for machine learning engineer or machine learning scientists to be successful, when you're running a production system, they have to really appreciate what constraints are there, you know, required online. So you have to look at how much CPU you use, how much memory you need, how fast can your model inference run with your model. And so they have to work very closely with the soft, soft engineering field. But at the same time, the software engineering guys need to know that their job is not to constrain the machine learning scientists. So, you know, as the models get larger, they have to get more creative. Right. And if that balance is right, then you get a really ambitious product. If that balance is not right, then you end up with a very small micro micro system. And so my job is to really make sure that the team is really ambitious in their thinking, not always liking, pushing the borders of what can be done. >>I like that pushing the borders of what can be done. You know, we, we often, when we talk about roles in, in stammered technology, we've talked about the hard skills, but the soft skills you've mentioned creativity. I always think creativity and curiosity are two soft skills that are really important in data science and AI. Talk to me about what your thoughts are. There >>Definitely creativity, because a lot of the problems that you, you know, when you're in school, the problems you face are very theoretical problems. And when you go into the industry and you realize that you need to solve a problem using the theory you learned, then you have to either start making different kinds of assumptions or realize that some assumptions just can be made because life is messy and online. You know, users are messy. They don't all interact with your system the same way. So you get creative in what can be solved. And then what needs to be controlled and folks who can't figure that piece out, they try to solve everything using machine learning, and they become a perfectionist, but nothing ever gets done then. So you need this balance and, and creativity plays a huge role in that space. And collaboration is you're always working with a diverse group of people. So explaining the problem space to someone who's selling your product, say someone is, you know, you build this automated bidding engine and they have to take this full mouth full and sell it to a customer. You've got to give them the terminology to use, tell, explain to them what are the benefits if somebody uses that. So I, I feel people who can empathize with the fact that this has to be explained, do a lot better when they're working in a product system, you know, bringing machine learning to a production system. >>Right. There's a lot of enablement >>There. Yes, exactly. Yeah. Yeah. >>Were you always interested in, in stem and engineering and AIS from when you were small? >>Somewhat? I mean, I've been, I got to my college degree. I was very certain by that point I wanted to be an engineer and my path to AI was kind of weird because I didn't really want to do computer science. So I ended up doing electrical engineering, but in my last year I did a project on speech recognition and I got introduced to computer programming. That was my first introduction to computer programming at the end of it, I knew I was going to work in the space. And so I came to the U S with less than three or four months of a computer engineering background. You know, I barely knew how to code. I had done some statistics, but not nearly enough to be in machine learning. And, but I landed in a good place. And I came to be in Boston university and I landed in a great lab. And I learned everything on my feet in that lab. I do feel like from that point onwards, I have always been interested and I'm never satisfied with just being interested in what's hot right now. I really want to know what can be solved later in the future. So that combination, I think, you know, really keeps me always learning, growing, and I'm never happy with just what's being done. >>Right? Yeah. We here, we've been hearing a lot about that today at weds. Just the tremendous opportunities that are here, the opportunities for data science, for good drones, for good data science and AI in healthcare and in public transportation. For example, you've been involved in with winds from the beginning. So you've gotten to see this small movement grow into this global really kind of is a >>Phenomenon. It is, >>It's a movement. Yes. You talk to me about your involvement with winds from the beginning and some of the things that you're helping them do. And now, >>So I, I first met Karen and marble initially when I was trying to get students from ICME to apply for roles in Microsoft. I really thought they had the right mix of applied and research mindset and the skill sets that were coming out of ICME rock solid in their math and theoretical foundations. So that's how I got to know them. And then they were just thinking about bids at that point in time. And so I said, you know, how can I help? And so I think I've been a keynote speaker, Pam list run a workshop. And then I got involved with the woods high school volunteer effort. And I'd say, that's the most rewarding piece of my visit involvement. And so I've been with them every year. I never Ms. Woods. I'm always here. And I think it is, you know, Grace Hopper was the technology conference for women and, and it's, it's, it's an awesome conference. I mean, it's amazing to sit next to so many women engineers, but data science was a part of it, but not a critical part of it. And so having this conference, that's completely focused on data science and making it accessible. The talks are accessible, making it more personable to, to all the invitees here. I think it creates a great community. So for me, I think it's, I hope they can run this and grow this for >>Yeah. Over 200 online events this year in 60 countries, they're aiming to reach a hundred thousand people annually. It's, it's grown dramatically in a short time period. Yes, >>Absolutely. Yeah. It hasn't been that long. It hasn't been that long and every year they add something new to the table. So for this year, I mean last year I thought the high schoolers, they started bringing in the high schoolers and this year again, I thought the high school. >>Yeah, >>Exactly. And I think the mix of getting data science from across a diversity, because a lot of the conferences are very focused. Like, you know, they, they will be the focused on healthcare and data science or pure AI or pure machine learning. This conference has a mix of a lot of different elements. And so attendees get to see how it's something is being used in healthcare and how something is being used in recommendations. And I think that diversity is really valuable. >>Oh, it's hugely valuable that the thought diversity is this is probably the conference where I discovered what thought diversity was if only a few years ago and the power and the opportunities that it can unlock for people everywhere for businesses in any industry. Yes. >>I want to kind of play off one of the things you said before, you know, data science for good, the, the incredible part of data sciences, you can do good wherever you are with data science. So take online advertising, you know, we build products for all advertisers, but we quickly figured out that are really large advertisers. They have their own data science teams and they are optimizing and, you know, creating new ads and making sure the best ads are serving at all times. They have figured out, you know, they have machine learning pipelines, so they are really doing their best already. But then there's this whole tale of small advertisers who just don't have the wherewithal or the knowledge to do any of that. Now, can you make data, use data science and your machine learning models and make it accessible for that long table? Pretty much any product you build, you will have the symptom of heavy users and then the tail users. And can you create an experience that is as valuable for those tailored users as it is for the heavy users. So data science for good exists, whatever problem you're solving, basically, >>That's nice to hear. And so you're going to be participating in some of the closing remarks today. What are some of the pearls of wisdom that you're going to enlighten the audience with today? >>Well, I mean the first thing I, to tell this audiences that they need to participate, you know, in whatever they shaped form, they need to participate in this movement of getting more women into stem and into data science. And my reasoning is, you know, I joined the lab and my professor was a woman and she was very strong scientists, very strong engineer. And that one story was enough to convince me that I belong. And if you can imagine that we create thousands of these stories, this is how you create that feeling of inclusion, where people feel like they belong. Yeah. Look, just look at those other 50 people here, those other a hundred stories here. This is how you create that movement. And so the first thing I want the audience to do is participate, come back, volunteer, you know, submit papers for keynote speeches, you know, be a part of this movement. >>So that's one. And then the second is I want them to be ambitious. So I don't want them to just read a book and apply the theory. I really want them to think about what problem are they solving and could they have solved it in the, in the scale manner that it can be solved. So I'll give a few examples and problems and I'll throw them out there as well. So for instance, experimentation, one of the big breakthroughs that happened in a lot of these large companies in data science is experimentation. You can AB experiment pretty much anything. You know, we can, Google has this famous paper where they talk about how they experimented with thousands of different blues just to get the right blue. And so experimentation has been evolving and data scientists are figuring out that if they can figure out interactions between experiments, you can actually run multiple experiments on the same user. >>So at any given time, you may be subject to four or five different experiments. Now, can we now scale that to infinity so that you can actually run as many experiments as you want questions like these, you shouldn't stop with just saying, oh, I know how AB experimentation works. The question you should be asking is how many such experiments can I run? How do I scale the system? As one of the keynote speakers initially talked about the unasked questions. And I think that's what I want to leave this audience with that don't stop at, you know, answering the questions that you're asked or solving the problems. You know, of you think about the problems you haven't solved your blind spots, you know, those blind spots and that I think I want ambitious data scientists. And so that's the message I want to give this audience. >>I can feel your energy when you say that. And you're involved with, with, with Stanford program for middle school and high school girls. If we look at the data and we see, there's still only about a quarter of stem positions are filled by females, what do you see? Do you see an inspiring group of young women in those middle school and high school girls that, that you see we're, we're on trend to start increasing that percentage. >>So I had a high schooler who just went, you know, she, she, she just, she's at UCLA now shout out to her and she, but she just went through high school. And what I realized is it's the same problem of not having enough stories around you, not having enough people around you that are all echoing the sentiment for, Hey, I love math. A lot of girls just don't talk about us. Yeah. And so I think the reason I want to start in middle school and high school is I think the momentum needs to start there. Yes. Because they get to college. And actually you heard my story. I didn't know any programming until I came here and I had already finished my four years of college and I still figured it out. Right. But a lot of women lose confidence to change fields after four years of college. >>Yes. And so if you don't catch them in early and you're catching them late, then you need to give them this boost of confidence or give them that ramp up time to learn, to figure out, like, I have a few people who are joining me from pure math nowadays. And these kids, these kids come in and within six months they're off and running. So, you know, in the interview phase, people might say, oh, they don't have any coding skills. Six months later, if you interview them, they pick up coding skills. Yeah. And so if you can get them started early on, I think, you know, they don't have this crisis of confidence of moving, changing fields. That's why I feel, and I don't think we are there yet, to be honest, I don't think yet. I think >>You still think there are plenty of girls being told. Now you can't do computer science. No, you can't do physics. No, you can't do math. >>Actually. They are denying it to themselves in many cases because they say, Hey, I go to physics class and there are two boys, two girls out of 50 boys. And I don't think girls are in, you know, you get the stereotype that maybe girls are not interested in physics. And it's not about, Hey, as a girl, I'm doing really well in physics. Maybe I should take this as my career. So I do feel we need to create more resounding stories in the area. And then I think we'll drum up that momentum. That's >>A great point. More stories, more and names to success here so that she can be what she can see exactly what many it's been great having you on the program. Thank you for joining me and sharing your background and some of the pearls of wisdom that you're gonna be dropping on the audience shortly today. We appreciate your insights. Thank you. My pleasure. Who Rick, Minnie, I are. I'm Lisa Martin. You're watching the cubes coverage weds 2022. We'll be right back after a short break.

>> 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)

>> Announcer: Live from Stanford University, it's theCUBE, covering the Women in Data Science Conference 2017. >> Hi, welcome to theCUBE. I'm Lisa Martin, and we are at the Women in Data Science second annual conference at Stanford University. Great event, very excited to be joined by one of the founders of the Women in Data Science, the Senior Director and Head of Data Science at Walmart Labs, Esteban Arcaute. Very nice to have you on the program. Thanks for joining us. >> Thank you for having me, Lisa. >> So talk to us about data science in retail. How is Walmart using data science too influence shoppers wherever they are, mobile, in store, dot com? >> So data science is a key component to how we create our experiences, especially now that our customers essentially don't really make a distinction between they're shopping in stores or they're actually using their mobile device, or they're at home with their desktop. So that means that for us it really is about creating a seamless experience that allows a customer to not feel that barrier of the medium that they're using to shop. So more practically, that means that the data that we're using to create the experience is essentially the same across all of these medias. >> So big data brings, and data science brings big opportunities, but also some challenges. Talk to us about some of the challenges that you've had with the tremendous amount of data because you've got what? Sixty million shoppers, 260 million, excuse me, globally. How are you dealing with some of those challenges and really turning them into opportunities to create that seamless experience? >> So for us it means that a lot of ready-made solutions that are available for other companies, they just don't work for us. The same way that other companies with large amounts of data, they actually have to create their own in-house solutions or technology. It is the same for us. Now in terms of how that is a very specific challenge, that means that when you actually go and train, let's say a model, that is trying to predict whether a customer is going to satisfied with a purchase or not, usually the amount of data that you have will make that model to not be that reliable unless you actually did it in-house. >> Okay, so from an accuracy perspective that really is what was driving being able to do that within Walmart Labs? >> Yes, and just sort of to give a plug to the department where I got my PhD, all of these numerical instabilities that in past you will only see when doing computational fluid dynamics, they actually start appearing in places like retail just because of the volume of data that is available. And so for us it's a great opportunity to be an ICME student. >> Excellent, and that's right, you got your Master's and your PhD right here at Stanford. Talk to us about from a scale and a speed perspective. How are you seeing the ability to influence the consumer experience? How quickly are you able to identify trends and act on them so that customer experience is better, and also the bottom line financials are improved as well for Walmart? >> That is a great question, Lisa, because our customers' expectations are changing really, really rapidly. If you remember back in the late 90s when you would go to a search engine and it worked, it was like a miracle. Everybody was really excited. Fast forward to today, you go to any search box, not a search engine, you put in a query. If it doesn't work, you're disappointed. When it works, it's just table stakes. That means that for us we need to be able to iterate as quickly as the customer expectations change, which is really, really fast. >> Absolutely. How do you collaborate with the business side? So first, let's talk about your team. What's the size of your team? As the head of data science, what are the different functions within your team, first and foremost? >> I'm also in charge of the search experience within Walmart Global eCommerce. It's a fairly large team because it is composed of basically the full stack from the back end, data science, dev ops, product management, so I cannot give you an exact size, but it's a fairly large team. >> And so how do you collaborate with the business to influence merchandising, for example? What is that collaboration like between Walmart Labs and the dot com side? >> So last year, Kelly Thompson was one of the speakers at the Women in Data Science Conference, and she talked about the importance of bringing the art of merchandising with the science of data science together. And it really is true that there're certain things that algorithms cannot catch as soon as a human expert actually knows about. And so the way we develop our products and enhance experiences for our customers is really bringing these two together in a partnership to ensure that there's never one side that is working on something that the other one cannot just leverage. >> From a priority perspective, how are some of the trends that you find driving priorities for investment? >> It goes both ways. Sometimes we find the trend. Sometimes the business finds the trend. And so sometimes the business asks us to try to automate or to predict something that we hadn't thought about, and that is actually very difficult, and hence we invest a lot in that. And sometimes we find some customer patterns that indicate a different behavior in a locality or with certain characteristics that then the business can go and better serve themselves. So it really is driven by whoever has a good idea, and they can come from anywhere. >> You mentioned the need still for human insight. Talk to us about that dynamic, machine learning and human insight. How does that work together, and again kind of thinking in the context of speed and skill to meet those changing customer demands? >> That is one of the best kept secrets for machine learning, is that most machine learning systems, the moment they have a human in the loop, the learning grade gets accelerated exponentially bcause essentially when a machine learning method is not working properly, it tends to be for certain types of cases that if they get resolved, just a few insights from a human being can actually go and make the machine learn a lot faster than if it's trying to figure it out on its own. So for us really even there is a partnership. We think of it as a systems with a human in the loop. That human, if it's an expert, it's even better, which is what we have. And so we create our systems to deeply integrate our merchandising capacity. >> So you actually see human intervention or interaction as a necessary component to speed to market leveraging data? >> That is the fastest way to get there. There might be other ways to do with that. We don't always have a human in the loop, but when we can have a human in the loop, we have seen that acceleration is actually measurable. >> Fantastic. So one of the things I wanted to chat about with you is looking at your team a little bit, as well as your involvement here in the Women in Data Science. You were one of the founders. Talk to us about Walmart's interest in helping to not only educate women, and further their education in data science, but also maybe to combat the predicted shortage of data scientists that's predicted to start even in 2018. How is that collaboration going to help in that sense? >> So let me address the question in two parts. First, the question related to women and minorities into data science. So Walmart is a very inclusive company. We win awards every year because of all of our work in there. And I think that starting with Women in Data Science, it's a natural place to start because there's always 50% of women everywhere. And so that means that really thinking that there should be an equal representation, or maybe not equal representation, there should be a way to funnel all of this talent into data science just makes sense. There's not a question as to whether there's sufficiently many of them or things like that. >> So culturally it was kind of a natural extension for Walmart Labs it sounds like. >> Absolutely, yes. And the second question is the shortage. So for us we're very lucky in that we have two things that any company needs to have to attract great data scientists. So first one is that we actually have data. Believe it or not, it is an asset that a lot of companies don't realize is actually (mumbling). And the second one is that we empower all of our associates with the ability to have impact from the get go. We don't put them in some small project that might have an impact in maybe three years. No, we actually put them in participating projects that might have, for instance in my team, impact within the first three to four months of being on the floor. >> That's fantastic, and I'm sure that really inspires them. They see that they can make an impact right away. And I would imagine just after chatting with you that they have the freedom probably to test and fail, and from that failure it becomes more opportunities to get and tweak and get things right. >> Absolutely. So especially in a field like retail, there's no laws of retail. There's not someone that just put in some nice equations and we just and study and do something. Actually you need to test over and interate constantly, especially when your customers expectations change so rapidly. >> So in terms of evolution of data science and skills, data presentation skills, analysis, stats, math, what are some of the other skills, maybe even social skills that you think are really key for the young next generation of data scientists to really get into this field regardless of industry and be successful? >> It's a question that I get very often, and especially because data science has not yet been formally properly defined in some sense. Data scientist is even less properly defined, so the term just started in 2010 or 11, so usually people think that they have to be hackers, have analytical skills and have some domain expertise. We actually flip that to say you have to have analytical skills, so that stays. You have to be a software engineer or have software engineering skills, and you have to project management skills. And the reason is that unless you are able to properly communicate what your insights are, to understand how they get incorporated into a real software system, and of course to have the expertise to know what you are doing, you're not going to be successful as a data scientist. So for us really those three components are the ones that drive what are we looking at data scientists. >> Excellent, so you mentioned hackers. Hackathons, you recently had a hackathon. How is Walmart Labs giving opportunities to maybe kids in grade school and high school, kids that are university to start developing that talent. >> So we have also an internship program every year. We have interns across all of Walmart Labs, and there is always a great opportunity to seed fresh new ideas that come from our interns, so that happens every year. We organize hackathons in very targeted way in places where we see that there is demand to have these kind of events organized. So I think one that we have in our website is one from 2015 with Tech Crunch Disrupt. It's a big one, but we do other things as well. >> But that actually has the ability, someone who's made a big difference or won at a hackathon that Walmart Lab sponsors has the ability to actually influence Walmart. >> Absolutely because as I said a couple of minutes ago, great ideas come from anywhere. And hackathons are great places where you see all of these ideas bubbling, and that you might not even realize that oh, that opportunity is right there. Someone can see it, and wants it seen, everybody can see it. So it's a great place. >> But that's a great, from a cultural perspective what you're saying sounds fantastic, that you're, there's a culture within Walmart Labs and Walmart that really is not only diverse from women in the sciences as well, but also one that really encourages test it, try it, you can make an impact here. And I think that's huge for attracting talent. What advice would you give to some of the young women that are here at the Women in Data Science Conference for the second annual to want to become successful data scientists? >> So I would give the advice that I have for myself, which is stay true to yourself, and anyone can be a great data scientist. >> What are some of the things that you're most looking forward to learning and hearing at this second annual event? >> The line up of speakers is amazing, and I think that the fact that they come from all places in industry, and all types of academic and professional journeys make it a very rich experience even for me to understand what are the possibilities. >> Absolutely, the cross section of speakers at the event is amazing. You've got obviously you know, data science into retail. We've got people that are using, that are going to be on the show later, data science to change the way college kids are recruited for jobs. Kind of getting away from that things that used to scare me, GPA, test scores, really leveraging science to open up those possibilities. And I think one of the things that that can enable from your comment earlier is the importance of being able to be a good communicator. It's not just about understanding the data. You've got to be able to explain it in a way that makes sense. Is this an impact? Also you mentioned we've got people that are here today on the academic side that are helping to educate the next generation of computer and data scientists. So I think it's a phenomenal opportunity for women of all ages to really understand it's not just technology. Every company this day and age is a technology company, and the opportunities are there to be influencers, and it sounds like at Walmart Labs, from the ground up. >> Yes, absolutely. >> Fantastic. Well, Esteban it's been such a pleasure having you on the program today. Thank you so much for joining. We look forward to having a great event and hopefully seeing you at the third annual next year. >> Definitely. Thank you very much for having me, Lisa. >> And you've been watching theCUBE. We are live at the Women in Data Science Conference at Stanford University. Stick around, be right back. (jazzy music)