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EVENT DETAILS |
Startups are agile & can move quickly, but often don't have significant historical data or many users generating new data. So what does "data science" at an early-stage startup look like? How does it change as the startup grows? What should you ask before joining a startup as their first data scientist?
Lisa Burton will discuss data science for startups, from the perspectives as both a founder & a data scientist. She'll then be joined by Kishau Rogers, the founder & CEO of Time Study, for a fireside chat to learn more about how Kishau integrated data into her product from day one & what she looks for when hiring her initial data science team.
Kishau Rogers is the Founder & CEO of Time Study. Time Study's mission is to eliminate time sheets through machine learning & mobile technology to automatically identify how employees spend their time, starting with health systems. Time Study is live with over 15,000 end users at health systems like NewYork-Presbyterian & Stony Brook Medicine. Kishau is a serial entrepreneur with over 20 years of experience developing software for hospitals. She previously founded Websmith, creating software solutions for partners from health & wellness agencies to non-profits, & PeerLoc, a technology startup providing a location services platform for indoor & other GPS-denied environments.
Lisa Burton, PhD is the Executive Director of HearstLab, a community of early stage, women-led startups innovating in media, data & technology. HearstLab provides assistance with building teams & refining products, along with office space in their New York City offices. At HearstLab, Lisa meets with prospective startups & supports the portfolio companies in residence, including advising on data & data science. Throughout her career, Lisa has built & led data science programs at startups & as a consultant across diverse industries -- from mobile payments to advertising to healthcare. Most recently, she cofounded a startup that leveraged data from social media to help brands understand & connect with their customers. Lisa came to data science from Mechanical Engineering, where she specialized in data-driven modeling & machine learning to predict the motion of swimming animals. She holds a PhD & SM from MIT & BSE from Duke.
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