 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. We've been here all day at the third annual Women in Data Science Conference with 2018. This event is remarkable in its growth and 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 in Stores as well from Walmart Labs. Vijay, welcome to theCUBE. Thank you, thank you for having me. Walmart Labs has been paramount to the success of WIDD. We had Margo Garrison on earlier and I said, how did you get the likes of a Walmart Labs as a partner and she was telling me the coffee shop conversation 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 to reach. So tell me a little bit about the from Walmart Labs perspective, the partnership with WIDDs. What is it that really kind of was an aha, we've got to do this? Yeah, it's just incredible seeing all these women and women data scientists here. It all started with Esteban Arcate who used to lead data science at Walmart Labs and search before he moved on to Facebook with Margo 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 with in Walmart but more specifically how we get more women into data science and that was really the genesis of that and it was really as credit goes to Esteban Margo 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. Margo was when we were chatting earlier she was saying it's still sort of surprising and she said she's been I think in the industry for 30 plus years and she said it, 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 it's 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 live stream that Woods is doing as well as their Facebook live stream 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 the team with 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 permeates 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, a lot of moms, a 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 as 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 a box as a data scientist that sounds like 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 on 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 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 the industry for a while get inspired and maybe get that fire relit underneath them. Yeah. 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 found, especially with the way we are leveraging data and how that is invigorating, the way we are, people come to the table is really almost seeing the art of what is possible. We are able to, 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 skill set, we talk a lot about the obvious technical skill set 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 and 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 it maybe in somewhat of a 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 Woods 2018 is the first annual Datathon. And Marga 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 the 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 bias way. What is your recommendation on and 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 bias is towards using data and data science to solve real problems and as opposed to pure research, 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 guide rails, you limit the way in which they would explore the data that they may come up with insights that we may not see otherwise and which is why I go back to the point I made when you have data scientists who fundamentally understand the 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 may 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 theCUBE. 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 theCUBE, live from Stanford University at 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.