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