>> 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)

(upbeat music) >> Welcome, everyone, to this CUBE Conversation here in Palo Alto, California. I'm John Furrier, host of the CUBE. We're here to talk about the state of MarTech and AI. We're here with the co-founder and head of AI for Blueshift, Manyam Mallela. Welcome to the CUBE, thanks for coming on. >> Thank you, John. Thank you for having me, excited to chat with you. >> Blueshift is a company you've co-founded with a couple other co-founders and you guys have a stellar pedigree going in data AI back before it was fashionable, in the old days, Web 1.0, if you want to call it that. So, you know, we know what you guys have been doing in your careers. Now you got a company on the cutting edge, solving problems for customers as they transition from this new, new way of doing things where users have data and power and control, customers are trying to be more authentic, got walled gardens emerging everywhere but that we're supposed to be away from walled gardens. So there's a whole set of new patterns, new expectations and new behaviors. So all this is challenging, but yet it's an opportunity. So I want to get into it. What is your vision? And what's your view on the MarTech today and AI, and how do you guys fit into that, that story? >> Yeah. Great question, John. We are still in the very early innings of where every digital experience is informed, both creatively from the marketing side of our organization, as well as the AI doing the heavy lifting under the herd to be able to create those experience at scale. And I think today every digital customer and every user out there are leaving a trail of very rich, very frequent interaction data with their brands and organizations that they interact with. You know, if you look at each of us, many, many moments and hours of our digital lives are with these interactions that we do on screens and devices, and that leaves a rich trail of data. And brands that are winning, brands that we want to interact with more, have user privacy and user safety at the center of it. And then they build that authentic connection from there on. And, you know, just like when we log into our favorite streaming shows or streaming applications, we want to see things that are relevant to us. They, in some sense, knowing kind of intimately our preferences or changing taste. And how does a brand or organization react to that but still make room for that authentic connection? >> It's an awesome opportunity. And it's a lot of challenges, and it's just starting, I totally agree. Let me ask you a question, Manyam, if you don't mind. How did you guys come up with Blueshift? I know you guys have been in this game before it was fashionable, so to speak, but you know, solving Web 1.0, 2.0 problems. And then, you know, Walmart Labs, everyone knows the history of Walmart and how fast they were with inventory and how they used data. You have that kind of trajectory. When you saw this opportunity, was it like the team was saying, wow, look at this, it's right in our wheelhouse, or, how did you guys get here, and then how did it all come together? >> Yeah, thanks for offering me an opportunity to share our personal journey. You know, I think prior to starting Blueshift with my co-founders, who I worked with for almost the past 20 years of my life, we were at a company called Kosmix, which was a Silicon Valley, early AI pioneer. We were doing semantics search, and in 2011, Walmart started their Silicon Valley innovation hub, Walmart Labs, with the acquisition of Kosmix. And, you know, we went into Walmart Labs, and until then they were already an e-commerce leader. They had been practicing e-commerce for better part of 12 years prior to that, but they're certainly you know, behind, compared to their peers, right? And the peers to be named! (laughs) But, they saw this lack of what it is that they were doing so well in brick and mortar that they're not able to fully get there on the digital side. And, you know, this was almost a decade ago. And when they brought in our team with a lot of AI and data systems at scale, building things at the cutting edge, you know, we went into it a little bit naively, thinking, you know, hey, we are going to solve this problem for Walmart scale in three months. (laughs) But it took us three years to build those systems of engagement. Despite Walmart having an enormous amount of resources being the number one retailer in the world and the data and the resource at their disposal, we had to rethink a lot of assumptions and the trends that were converging were, you know, uses for interacting with them across multiple formats and channels. And both offline and online, the velocity and complexity of the data was increasing. All the marketing and merchandising teams said even a millisecond delay for me is unconscionable. And how do you get fresh data and activated at the moment of experience, without delay, this significant challenge at scale? And that's what we solve for our organizations. >> It really is the data problem. It's a scale problem. It's all that. And then having the software to have that AI predictive and, you know, it's omnichannel when you think about it, in that retail and that brick and mortar term used for physical space and digital converging. And we saw the pandemic pull forward this same dynamic where events and group behaviors and just interactions were all converging. So this line between physical and digital is now blurred, completely blended, the line between customer experience and marketing has been erased, and you guys are the center of this. What does it mean for the customer? Because the customers out there, your customers, or potential customers. They got problems to solved. They're going all digital cloud-native applications, the digital transformation. This is the new normal, and some are on it, are starting it, some are way behind. What are they- What's the situation with the customers? >> Yeah, that's certainly the maturity of, you know, the, each brand and organization along that, you know, both transformation and from transformation to actually thriving in that ecosystem. And how do we actually win, you know, share of mind and then share of, like, that market that they're looking to does take a while. And, and many are, you know, kind of midway through their journey. I think, there was, initially there is a lot of, you know, push towards let's collect all the data that we can but then, you know, how does the actually data becomes something useful that changes experience for Manyam versus John is really that critical moment. And that moment is when, you know, a lot of things come into place. And if I look at, like, the broader landscape, there are certainly lines of powers like Discovery, like Udacity and LendingTree, and Zumper car pods across all these industries. Who would've thought like, you know, all these industries who you would not think of actually as solving a digital engagement problem are now saying that's the key to our success and our growth. >> Yeah. It's absolutely the number one problem. This is the number one opportunity for all businesses, not just verticals here and there, all verticals. So walk me through your typical customer scenario. You know, what are the challenges that they face? You're in the middle of it, you're solving these problems, what are their challenges that they face and how do you guys solve them? >> Absolutely. So I'll talk through two examples, one from a finance industry, one from online learning, you know, o One of our great customers that we partner with is LendingTree. They offer tens of millions of customers' finance products that span from home loans, students loans, auto loans, credits, all of that. And, and let these people come into their website and collect information that is relevant to the loan that they're considering, but engage them in a way for the next period of time. So if you typically think about engagement, it's not just a one interaction, usually that follows a series of steps an organization has to take to be able to explain all their offerings in a way that is digestible and relevant and personalized to each of those millions of customers and actually have them through the funnel and measure it and report on it and make sure that that is the most relevant to them. So in a finance setting that is about consuming credit products, consuming loan products, consuming reporting products in an online context. I'll give you an example of one of our customers, Udacity. Imagine you are a marketing team of two people, and you are in challenged with, how do you engage 20 million students. You're not going to write 20 million communications that are different for each of those students, certainly. I think you need a system to say what did actually all these students come for? How do I learn what they want at this moment in time? What do they want next? If they actually finished something that they started two months ago, would they be eligible for the right course? Maybe today we are talking about self-driving cars. That's the course that I should bring in front of them. And that's only a small segment of the students but someone else maybe on the media and the production side. How do I personalize the experience so that every single step of the way for that student is, you know, created and delivered at scale? And that's kind of the problem that we solve for our brands, which is they have these millions of touchpoint that are, that they have, how do they bring all their data, very fresh and activated at the moment of action? >> So you guys are creating the 10x marketer. I mean, kind of- >> That's right. That's a very (indistinct)- >> 10X engineer, the famous, you're 10X engineer. >> Right. >> You guys are bringing a lot of heavy lifting to short staffs or folks that don't have a data science team or data engineering team. You're kind of bringing that 10x marketing capability. >> Absolutely. I think that's a great way to put it. I call it the mission impossible, which is, you know, you're signing up for the mission impossible, for every marketing team, it's like, now they're like, they are the product managers they're the data scientists, they're the analysts. They are the creator, you know, author, all of that combined into a role. And now you're entrusted with this really massive challenge. And how do you actually get there? And it's that 10x marketer who are embracing these technologies to get there. >> Well, I'm looking forward to challenging though because I can imagine you get a lot of skeptics out there. I don't believe you. It sounds too good to be true. And I want to get to that in the next segment, but I want to ask you about the state of MarTech and AI specifically. MarTech traditionally has been on Web 2.0 standards, DNS, URLs. It's the naming system of the internet. It's the internet infrastructure. So- >> Right. what needs to change to make that scale higher? Does, is there any new abstraction or any kind of opportunities for doing things in just managing you know, tokens that need to be translated? It's hard to do cross to- I mean, there's a lot of problems with Web 2.0 legacy that kind of holds back the promise of high availability of data, privacy, AI, more machine learning, more exposure of data. Can you share your vision on this next layer? >> Absolutely. Yeah, I think, you know, there's a lot of excitement about what Web3 would bring us there in the very early innings of that possibility. But the challenge of, you know, data that leads to authentic experience still remains the same whichever metaverse we might actually interact with a brand name, like, you know, even if I go to a Nike store in the Metaverse, I still need to understand what that customer really prefers and keep up with that customer as they change their preferences. And AI is the key to be able to help a marketer. I call it the, you know, our own group call it like IPA you know, which is ingest all possible data, even from Metaverse, you know, the protocols might change, the formats might change, but then you have to not only have a sense of what happened in the past. I think there are more than enough tools to know what happened. There are only emerging tools to tell you what might happen. How do I predict? So ingest, predict, and then next step is activate. Actually you had to do something with it. How do I activate it, that the experience for you, whether it's Web3 or Web2 changes, and that IPA is kind of our own brew of, you know, AI marketing that we are taking to market. >> And that's the enablement piece, so how does this relate to the customer's data? You guys are storing all the data? Are they coming in? Is there a huge data lake involved? Can I bring in third party data? Does it have to be all be first party? How is that platform-level enabling this new form of customer engagement? >> Absolutely. There's a lot of heavy lifting that the data systems that one has to you know, bring to bear upon the problem, data systems ranging from, you know, distributed search, distributed indexing, low latency systems, data lakes that are built for high velocity, AI machine learning, training model inference, that validation pipeline. And, you know, we certainly leverage a lot of of data lake systems out there, including many of the components that are, you know, provided by our preferred partner, AWS and open source tools. And these data systems are certainly very complex to manage. And for an organization that, with a, you know, 5 to 10 people team of marketers, they're usually short staffed on the, the amount of attention that they get from rest of the organization. And what we have made is that you can ingest a lot more raw data. We do the heavy lifting, but both data management, identity resolution, segmentation, audience building, predictions, recommendations, and then give you also the delivery piece, which is, can I actually send you something? Can I put something in front of the user and measure it and report on it and tell you that, this is the ROI? How do, if all this would be for nothing, if actually you go through all this and there's no real ROI. And we have kind of, you know, our own forester did a total economic impact study with us. And they have found, they have found 781% ROI for implementing Blueshift. And it's a tremendous amount of ROI you get once you are able to reorient your organizations towards that. >> You know, Manyam, one of the problems of being a visionary and a pioneer like you guys are, you're early a lot. And so you must be scratching your head going, oh, the hot buzzword these days is the semantic layer, in Khan, you see snowflake and a bunch of other people kind of pushing this semantic layer. It's basically a data plane essentially for data, right? >> Right. >> And you guys have done that. Been there, done that, but now that's in play, you guys have this. >> That's right. >> You've got all this semantic search built in into the system, all this in data ingestion, it's a full platform. And so I need to ask you how you see this vectoring into the future state of customer engagement. Where, where do you see this intersecting with the organizations you're trying to bring this to? Are they putting more investment in, are they pulling back? Are they, where are, where are they and where are you guys relative to this, this technology? And, and, and, and first of all let's get your reaction to this semantic layer first. >> Right, right. It's a fantastic, you know, as a technologist, I love, you know, kind of the ontology and semantic differences, you know, how, how, you know, data planes, data meshes, data fabrics are put together. And, you know, I saw this, you know, kind of a dichotomy between CIO org and CMO org, right? The CO says like, you know, I have the best data plane, the data mesh, the data fabric. And the CMO says like, but I'm actually trying to accomplish something for this campaign. And they're like, oh, that, does it actually connect the both of pieces? >> So I think, the- >> Yeah? >> The CMO org certainly will need purpose-built applications, on top of the data fabric, on top of the data lakes, on top of the data measures, to be able to help marketing teams both technical and semi-technical to be able to accomplish that. >> Yeah. And then, and the new personas they want turnkey, they want to have it self-service. Again, the 10x marketer is someone with a small staff that can do the staff of hundred people, right? >> That's absolutely- >> So that's where it's going. And this is, this i6s the new normal. >> So, we call them AI marketers. And I think it's a, it's like you're calling a 10x marketer. I think, you know, over time we didn't have, you know this word, business intelligence analyst, but then once the tool are there, then they become business intelligence analysts. I think likewise, once these tools are available then we'll have AI marketers out in the market. >> Well, Manyam, I'd love to do a full, like, one-hour podcast with you. You can go for a long time with these topics given what you guys are working on, how relevant it is, how cool it is right now, and with what you guys have as a team and solution. I really appreciate you coming on the CUBE to chat. For the last minute we have here, give a quick plug for the company, what you guys are up to, size, funding, revenues, what you're looking for. What should people pay attention to? Give the plug. >> Yeah. Yeah, we are a global team, spanning, you know, multiple time zones. You know, we have raised $65 million to date to build out our vision and, you know, over the last eight years of our funding, we have served hundreds of customers and continuing to, you know, take on more. I think, you know, our hope is that over time, the next 10,000 organizations see this as a very much an approachable, you know, problem to solve for themselves, which I think is where we are. AI marketing is real doable, proven ROI. Can we get the next 10,000 customers to embrace that? >> You know, as we always used to say in the kind of web business and search, it's the contextual and the behavioral, you got to bring 'em together here. You got all that technology for the, for the sites and applications for the behavior and converting that contextually into value. Really compelling solution. Thanks for sharing your insight. >> Yeah. Thank you John, really appreciate this. >> Okay, this is CUBE Conversation. I'm John Furrier here in Palo Alto. Thanks for watching. (upbeat music)

>>live from Stanford University. It's the queue covering Stanford women in data Science 2020. Brought to you by Silicon Angle Media. >>Hi, and welcome to the Cube. I'm your host, Sonia today, Ari. And we're live at Stanford University covering wigs, Women in Data Science Conference 2020 And this is the fifth annual one. Joining us today is John Hoegger, who is the principal data scientist manager at Microsoft. John. Welcome to the Cube. Thanks. So tell us a little bit about your role at Microsoft. >>I manage a central data science team for myself. 3 65 >>And tell us more about what you do on a daily basis. >>Yeah, so we look at it across all the different myself. 365 products Office Windows security products has really try and drive growth, whether it's trying to provide recommendations to customers to end uses to drive more engagement with the products that they use every day. >>And you're also on the Weeds Conference Planning Committee. So tell us about how you joined and how that experience has been like, >>Yeah, actually, I was at Stanford about a week after the very first conference on. I got talking to Karen, one of this co organizers of that that conference and I found out there was only one sponsor very first year, which was WalMart Labs >>on. >>The more that she talked about it, the more that I wanted to be involved on. I thought that makes it really should be a sponsor, this initiative. And so I got details. I went back and my assessment sponsor. Ever since I've been on the committee trying it help with. I didn't find speakers on and review and the different speakers that we have each year. And it's it's amazing just to see how this event has grown over the four years. >>Yeah, that's awesome. So when you first started, how many people attended in the beginning? >>So it started off as we're in this conference with 400 people and just a few other regional events, and so was live streamed but just ready to a few universities. And ever since then it's gone with the words ambassadors and people around the world. >>Yes, and outwits has is over 60 countries on every continent except Antarctica has told them in the Kino a swell as has 400 plus attendees here and his life stream. So how do you think would has evolved over the years? >>Uh, it's it's term from just a conference to a movement. Now it's Ah, there's all these new Our regional events have been set up every year and just people coming together, I'm working together. So, Mike, self hosting different events. We had events in Redmond. I had office and also in New York and Boston and other places as well. >>So as a as a data scientist manager for many years at Microsoft, I'm I'm sure you've seen it increase in women taking technical roles. Tell us a little bit about that. >>Yeah, And for any sort of company you have to try and provide that environment. And part of that is even from recruiting and ensuring that you've got a diverse into s. So we make sure that we have women on every set of interviews to be able to really answer the question. What's it like to be a woman on this team and your old men contents of that question on? So you know that helps as faras we try, encourage more were parented some of these things demos on. I've now got a team of 30 data scientists, and half of them are women, which is great. >>That's also, um So, uh, um, what advice would you give to young professional women who are just coming out of college or who just starting college or interested in a stem field? But maybe think, Oh, I don't know if they'll be anyone like me in the room. >>Uh, you ask the questions when you interview I go for those interviews and asked, like Like, say, What's it like to be a woman on the team? All right. You're really ensuring that the teams that you're joining the companies you joined in a inclusive on and really value diversity in the workforce >>and talking about that as we heard in the opening address that diversity brings more perspectives, and it also helps take away bias from data science. How have you noticed that that bias becoming more fair, especially at your time at Microsoft? >>Yeah, and that's what the rest is about. Is just having those diverse set of perspectives on opinions in heaven. More people just looking like a data and thinking through your holiday to come. Views on and ensure has been used in the right way. >>Right. Um and so, um, what do you going forward? Do you plan to still be on the woods committee? What do you see with is going how DC woods in five years? >>Ah, yeah. I live in for this conference I've been on the committee on. I just expected to continue to grow. I think it's just going right beyond a conference. Dossevi in the podcasts on all the other initiatives that occurring from that. >>Great. >>John, Thank you so much for being on the Cube. It was great having >>you here. Thank you. >>Thanks for watching the Cube. I'm your host, Sonia, to worry and stay tuned for more. Yeah.

>> 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)

>> 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)

>>Hi. Welcome to the Cube. I'm Lisa Martin on the ground at the Computer History Museum with the Association for Corporate Go Silicon Valley. Tonight is their 13th annual grow worth, and we're very excited to be with one of their pick sponsors. Deloitte Christie. Simon's from Deloitte. Welcome. Thank you. Great to have you here. So you are a veteran and technology. You've been in the tech industry over 25 years. You've probably seen incredible transformation. Tell us about what you're doing with Deloitte and the advisory service is not you. Offer way. Offer a number of service is advisory audit tax too in Silicon Valley to a lot of these emerging growth companies. So it's been very exciting >>in my >>career to see the evolution of what I call old technology right where we kind of got the traditional software semiconductor box companies to what is now digitally what I call a new technology and what is propelling the economy in the throat that we're seeing. Not only in Bali. Exactly. So right now you are working, leading hurt and development of Deloitte's technology practice up in San Francisco. You're working with clients and you mentioned digital and clown Internet media sectors tell us about that, especially as you mentioned new technology. So a lot of them are startup companies, which is really sweet spotted, A C G. And that's why we're so involved with a G. But a lot of these new technology companies that you mentioned, you know, cloud software service, Internet media, data security, those types of companies, eyes really propelling the digital economy. So we see a lot of growth in that sector, primarily in San Francisco but also in the broader Bay Area. Silicon being checked better and as you are you mentioned out of what's going on domestically but also internationally. How do you see the influence of Silicon Valley here in Silicon Valley as well as across the globe? You know, there's a lot of factors weigh serve companies all over the globe. So primarily, Silicon Valley is propelling a lot of those. And to the extent that companies here are international, most of a lot of multinational companies and do sell their products lovely there, developed here with products are actually sold. Are you seeing kind of the inverse where companies may be headquartered in in Europe or Asia? are influencing and bringing technology over to the Silicon Valley. Next thing, let us here. Yeah, some of that, especially as we think about, uh, engineers and the aspects and some of that development that happens there, obviously sourcing that from around interest of an industry perspective in 2017. It's like every company's tech way. Look at tests around the street. Look at Walmart Labs and what they're doing there. How are you seeing some of the clients you advise for? What are some of the industries that you're seeing are now technology? There's definitely a convergence says you mentioned Too many industries, actually, all industries. So when we think about financial service is no fintech. When you think about life sciences, health, when you think about retail, right, you got Internet. So definitely saying convergence and technology is impacting our daily lives and almost everything that we d'oh and in almost every product and service that we buy, there's some form or elements of technology. Exactly. It's really remarkable. Speaking of remarkable, tonight we're here with a C G to recognize two Fantastic Cos Twilio is the emerging growth winner, 2017 and video the Outstanding Growth Award winner. If you look at and video, for example, inventor of the GPS, which is really catalyzed a tremendous amount of technology across industries. If we were just talking about you, look at their market kind of what you see them over the next couple of years. The market drivers you think they're gonna impact mentioned and video write graphic way historically have been known for games and films and virtual reality kind of thing. Now they're actually moving more into artificial intelligence. Artificial intelligence? A. I knew Buzz Word, right? So there's probably a lot of opportunities for a video that technology evolves and develops over the next. Several questions for Twilio. Who's winning the emerging world? What would you do for them? So they're, you know, cloud platform company for software developers. So you think that part of the new technology is and a cloud, so providing an opportunity for engineers to develop software and software is involved in almost everything that we do as well in our daily lives. So you know that convergence of all the industries that's happening, a lot of that is a result of software and the developers who are creating that software Twilio is providing a platform for that communicated a tremendous opportunity. Companies in this new technology. Christy, thank you so much for joining us on the Cuban. Sharing your insight. Have a great evening tonight. Yes, it's, uh, it's a great turn out Isn't a lot of fun. It is. I want to thank you for watching way around the museum with a c D E f G. I'm Lisa Martin. Thanks.

>> Narrator: Live from Stanford University, it's theCUBE covering the Women in Data Science Conference 2017. >> Hi welcome back to theCUBE, I'm Lisa Martin and we are live at Stanford University at the second annual Women in Data Science one day tech conference. We are joined by one of the speakers for the event today, Claudia Perlich, the Chief Scientist at Dstillery, Claudia, welcome to theCUBE. >> Claudia: Thank you so much for having me. It's exciting. >> It is exciting! It's great to have you here. You are quite the prolific author, you've won data mining competitions and awards, you speak at conferences all around the world. Talk to us what you're currently doing as the Chief Scientist for Dstillery. Who's Dstillery? What's the Chief Scientist's role and how are you really leveraging data and science to be a change agent for your clients. I joined Dstillery when it was still called Media6Degrees as a very small startup in the New York ad tech space. It was very exciting. I came out of the IBM Watson Research Lab and really found this a new challenging application area for my skills. What does a Chief Scientist do? It's a good question, I think it actually took the CEO about two years to finally give me a job description, (laughter) and the conclusion at that point was something like, okay there is technical contribution, so I sit down and actually code things and I build prototypes and I play around with data. I also am referred to as Intellectual Leadership, so I work a lot with the teams just kind of scoping problems, brainstorming was may work or dozen, and finally, that's what I'm here for today, is what they consider an Ambassador for the company, so being the face to talk about the more scientific aspects of what's happening now in ad tech, which brings me to what we actually do, right. One of the things that happened over the recent past in advertising is it became an incredible playground for data signs because the available data is incomparable to many other fields that I have seen. And so Dstillery was a pioneer in that space starting to look at initially social data things that people shared, but over the years it has really grown into getting a sense of the digital footprint of what people do. And our primary business model was to bring this to marketers to help them on a much more individualized basis identify who their customers current as well as futures are. Really get a very different understanding than these broad middle-aged soccer mom kind of categories to honor the individual tastes and preferences and actions that really truly reflect the variety of what people do. I'm many things as you mentioned, I publish mom, what's a mom, and I have a horse, so there are many different parts to me. I don't think any single one description fully captures that and we felt that advertising is a great space to explore how you can translate that and help both sides, the people that are being interacted with, as well as the brands that want to make sure that they reach the right individuals. >> Lisa: Very interesting. Well, as buyers journey as changed to mostly online, >> Exactly. >> You're right, it's an incredibly rich opportunity for companies to harness more of that behavioral information and probably see things that they wouldn't have predicted. We were talking to Walmart Labs earlier and one of the interesting insights that they shared was that, especially in Silicon Valley where people spend too much time in the car commuting-- (laughter) You have a long commute as well by train. >> Yes. >> And you'd think that people would want, I want my groceries to show up on my doorstep, I don't want to have to go into the store, and they actually found the opposite that people in such a cosmopolitan area as Silicon Valley actually want to go into the store and pick up-- >> Claudia: Yep. >> Their groceries, so it's very interesting how the data actually can sometimes really change. It's really the scientific method on a very different scale >> Claudia: Much smaller. >> But really using the behavior insights to change the shopping experience, but also to change the experience of companies that are looking to sell their products. >> I think that the last part of the puzzle is, the question is no longer what is the right video for the Super Bowl, I mean we have the Super Bowl coming up, right? >> Lisa: Right. Right. >> They did a study like when do people pay attention to the Super Bowl. You can actually tell, cuz you know what people don't do when they pay attention to the Super Bowl? >> Lisa: Mm,hmm. >> They're not playing around with their phones. They're actually not playing-- >> Lisa: Of course. >> Candy Crush and all these things, so what we see in the ad tech environment, we actually see that the demand for the digital ads go down when people really focus on what's going on on the big screen. But that was a diversion ... >> Lisa: It's very interesting (laughter) though cuz it's something that's very tangible and very ... It's a real world applications. Question for you about data science and your background. You mentioned that you worked with IBM Watson. Forbes has just said that Data Scientist is the best job to apply for in 2017. What is your vision? Talk to us about your team, how you've grown that up, how you're using big data and science to really optimize the products that you deliver to your customers. >> Data Science is really many, many different flavors and in some sense I became a Data Scientist long before the term really existed. Back then I was just a particular weird kind of geek. (laughter) You know all of a sudden it's-- >> Now it has a name. (laughter) >> Right and the reputation to be fun and so you see really many different application areas depending very different skillsets. What is originally the focus of our company has always been around, can we predict what people are going to do? That was always the primary focus and now you see that it's very nicely reflected at the event too. All of sudden communicating this becomes much bigger a part of the puzzle where people say, "Okay, I realize that you're really "good at predicting, but can you tell me why, "what is it these nuggets of inside-- >> Interpretation, right. >> "That you mentioned. Can you visualize what's going on?" And so we grew a team initially from a small group of really focused machine learning and predictive skills over to the broader can you communicate it. Can you explain to the customer archieve brands what happened here. Can you visualize data. That's kind of the broader shift and I think the most challenging part that I can tell in the broader picture of where there is a bit of a short coming in skillset, we have a lot of people who are really good today at analyzing data and coding, so that part has caught up. There are so many Data Science programs. What I still am looking for is how do you bring management and corporate culture to the place where they can truly take advantage of it. >> Lisa: Right. >> This kind of disconnect that we still have-- >> Lisa: Absolutely. >> How do we educate the management level to be comfortable evaluating what their data science group actually did. Whether they working on the right problems that really ultimately will have impact. I think that layer of education needs to receive a lot more emphasis compared to what we already see in terms of this increased skillset on just the sheer technical side of it. >> You mentioned that you teach-- >> Claudia: Mm,hmm. >> Before we went live here, that you teach at NYU, but you're also teaching Data Science to the business folks. I would love for you to expand a little bit more upon that and how are you helping to educate these people to understand the impact. Cuz that's really, really a change agent within the company. That's a cultural change, which is really challenging-- >> Claudia: Very much so. >> Lisa: What's their perception? What's their interest in understanding how this can really drive value? >> What you see, I've been teaching this course for almost six years now, and originally it was really kind of the hardcore coders who also happened to get a PhD on the side, who came to the course. Now you increasingly have a very broad collection of business minded people. I typically teach in the part-time, meaning they all have day jobs and they've realized in their day jobs, I need this. I need that. That skill. That knowledge. We're trying to get on the ground where without having to teach them python and ARM whatever the new toys are there. How can you identify opportunities? How do you know which of the many different flavors of Data Science, from prediction towards visualization to just analyzing historical data to maybe even causality. Which of these tools is appropriate for the task at hand and then being able to evaluate whether the level of support that a machine can only bring, is it even sufficient? Because often just because you can analyze data doesn't mean that the reliability of the model is truly sufficient to support then a downstream business project. Being able to really understand those trade offs without necessarily being able to sit down and code it yourself. That knowledge has become a lot more valuable and I really enjoy the brainstorming when we're just trying to scope a project when they come with problems from their day job and say, "Hey, we're trying to do that." And saying, "Are you really trying to do that?" "What are you actually able to execute? "What kind of decisions can you make?" This is almost like the brainstorming in my own company now brought out to much broader people working in hospitals, people working in banking, so I get exposed to all of these kinds of problems said and that makes it really exciting for me. >> Lisa: Interesting. When Dstillery is talking to customer or prospective customers, is this now something that you're finding is a board level conversation within businesses? >> Claudia: No, I never get bored of that, so there is a part of the business that is pretty well understood and executed. You come to us, you give us money, and we will execute a digital campaign, either on mobile phones, on video, and you tell me what it is that you want me to optimize for. Do you want people to click on your ad? Please don't say yes, that's the worst possible things you may ask me to do-- (laughter) But let's talk about what you're going to measure, whether you want people to show up in your store, whether you really care about signing up for a test drive, and then the system automatically will build all the models that then do all the real-time bidding. Advertising, I'm not sure how many people are aware, as your New York Times page loads, every single ad slot on that side is sold in a real-time auction. About 50 billion times a day, we receive a request whether we want to bid on the opportunity to show somebody an ad. >> Lisa: Wow. >> So that piece, I can't make 50 billion decisions a day. >> Lisa: Right. >> It is entirely automated. There's this fully automated machine learning that just serves that purpose. What makes it interesting for me now that ... Now this is kind of standard fare if you want to move over and is more interesting parts. Well, can you for instance predict which of the 15 different creatives I have for Jobani, should I show you? >> Lisa: Mm,hmm. >> The one with the woman running, or the one with the kid opening, so there is no nuances to it and exploring these new challenges or going into totally new areas talking about, for instance churn prediction, I know an awful lot about people, I can predict very many things and a lot of them go far beyond just how you interact with ads, it's almost the most boring part. We can see people researching diabetes. We can provide snapshots to farmer telling them here's really where we see a rise of activity on a certain topic and maybe this is something of interest to understand which population is driving those changes. These kinds of conversations really making it exciting for me to bring the knowledge of what I see back to many different constituents and see what kind of problems we can possibly support with that. >> Lisa: It's interesting too. It sounds like more, not just providing ad technology to customers-- >> Claudia: Yeah. >> You're really helping them understand where they should be looking to drive value for their businesses. >> Claudia: That's really been the focus increasingly and I enjoy that a lot. >> Lisa: I can imagine that, that's quite interesting. Want to ask you a little bit before we wrap up here about your talk today. I was looking at your, the title of your abstract is, "Beware what you ask for: The secret life of predictive models". (laughter) Talk to us about some of the lessons you learn when things have gone a little bit, huh, I didn't expect that. >> I'm a huge fan of predictive modeling. I love the capabilities and what this technology can do. This being said, it's a collection of aha moments where you're looking at this and this, this doesn't really smell right. To give you an example from ad tech, and I alluded to this, when people say, "Okay we want a high click through rate." Yes, that means I have to predict who will click on an ad. And then you realize that no matter what the campaign, no matter what the product, the model always chooses to show the ad on the flashlight app. Yeah, because that's when people fumble in the dark. The model's really, really good at predicting when people are likely to click on an ad, except that's really not what you intended-- >> Right. >> When you asked me to do that. >> Right. >> So it's almost the best and powerful that they move off into a sidetracked direction you didn't even know existed. Something similar happened with one of these competitions that I won. For Siemens Medical where you had to identify an FMI images of breast, which of these regions are most likely benign or which one have cancer. In both models we did really, really well, all was good. Until we realized that the patient ID was by far the most predictive feature. Now this really shouldn't happen. Your social security number shouldn't be able to predict-- >> Lisa: Right. >> Anything really. It wasn't the social security number, but when we started looking a little bit deeper, we realized what had happened is the data set was a sample from different sources, and one was a treatment center, and one was a screening center and they had certain ranges of patient IDs, so the model had learned where the machine stood, not what the image actually contained about the probability of having cancer. Whoever assembled the data set possibly didn't think about the downstream effect this can have on modeling-- >> Right. >> Which brings us back to the data science skill as really comprehensive starting all the way from the beginning of where the data is collected, all the way down to be extremely skeptical about your own work and really make sure that it truly reflects what you want it to do. You asked earlier like what makes really good Data Scientists. The intuition to feel when something is wrong and to be able to pinpoint and trace it back with the curiosity of really needing to understand everything about the whole process. >> Lisa: And also being not only being able to communicate it, but probably being willing to fail. >> Claudia: That is the number one really requirement. If you want to have a data-driven culture, you have to embrace failure, because otherwise you will fail. >> Lisa: How do you find the reception (laughter) to that fact by your business students. Is that something that they're used to hearing or does it sound like a foreign language to them? >> I think the majority of them are in junior enough positions that they-- >> Lisa: Okay. >> Truly embrace that and if at all, they have come across the fact that they weren't allowed to fail as often as they had wanted to. I think once you go into the higher levels of conversation and we see that a lot in the ad tech industry where you have incentive problems. We see a lot of fraud being targeted. At the end of the day, the ad agency doesn't want to confess to the client that yeah they just wasted five million dollars-- >> Lisa: Right. >> Of ad spend on bots, and even the CMO might not be feeling very comfortable confessing that to the CO-- >> Right. >> Claudia: Being willing to truly face up the truth that sometimes data forces you into your face, that can be quite difficult for a company or even an industry. >> Lisa: Yes, it can. It's quite revolutionary. As is this event, so Claudia Perlich we thank you so much for joining us-- >> My pleasure. >> Lisa: On theCUBE today and we know that you're going to be mentoring a lot of people that are here. We thank you for watching theCUBE. We are live at Stanford University from the Women in Data Science Conference. I am Lisa Martin and we'll be right back (upbeat 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)