Dawn Woodard talks with Lisa Martin at WiDS 2018 at Stanford University. #WiDS2018 #theCUBE #WomenInTech https://siliconangle.com/2018/03/09/narrowing-the-gender-gap-through-exponential-mentoring-wids2018/ Narrowing Uber’s gender gap through exponential mentoring A passion for math and science started Dawn Woodard (pictured) on a career path in data science, but she counts herself fortunate to have had the support and mentorship of other women, including her mother, along the way. From her current leadership role as a woman in data science — she is the senior data science manager of maps at Uber Technologies Inc. — she focuses on reaching out not only to new hires, but to other women in leadership positions within Uber. “One of the things that I’ve really focused on is mentoring women as leaders and managers within my organization. … Then, of course, those female managers bring in additional female contributors, and it grows from there,” Woodard said. She is proud that her current team at Uber bucks the male-dominant trend in tech by being more than 50 percent women, and she acknowledges that her tenacity in nurturing talented women is part of the reason for this. “The first data scientist on maps at Uber was a woman,” she stated. Woodard spoke with Lisa Martin (@LuccaZara), host of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, during the Global Women in Data Science Conference in Stanford, California. They discussed Woodard’s talk at the WiDS conference, her journey to becoming head of Uber’s maps department, and her goals for the future. Data science is in everything “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?” Woodard asked. The interdisciplinary nature of data science was a focus of Woodard’s speech at WiDS 2018, titled “Dynamic Pricing and Matching in Ride Sharing.” The matching algorithms used by Uber are designed to reduce the amount of time that riders and drivers have to spend waiting in the app (the less time in the app, the less wait time for clients and more paid time for drivers), and dynamic pricing is critical for seamless and reliable experiences with a rideshare service, according to Woodard. The top two soft skills she looks for in candidates for the Uber maps team as impact and attitude. “When I’m hiring a data scientist, [I look for] being really focused on impact as opposed to focused on building a new, shiny thing, and also that great attitude about being willing to chip in on something, even if it’s not that fancy,” she concluded. Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of the Global Women in Data Science Conference. (* Disclosure: TheCUBE is a paid media partner for the Women in Data Science Conference. Neither Stanford University, the event sponsor, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)