DJ Patil, U.S. Chief Data Scientist at White House Office of Science and Technology Policy
DJ Patil is the chief data scientist and deputy chief technology officer for data policy at the White House Office of Science and Technology Policy, where he advises on policies and practices to maintain US leadership in technology and innovation, fosters partnerships to maximize the nation’s return on its investment in data, and helps to attract and retain the best minds in data science to serve the public. Since joining OSTP, DJ has collaborated with colleagues across government, including the chief information officer and the US Digital Service as part of the Obama administration’s commitment to open data and data science. He leads data science efforts related to the Precision Medicine Initiative, which focuses on utilizing advances in data and health care to provide clinicians with new tools, knowledge, and therapies to select which treatments will work best for which patients while protecting patient privacy.
DJ joined the White House following an incredible career as a data scientist—a term he helped coin—in the public and private sectors and in academia. Most recently, he served as the vice president of product at RelateIQ (acquired by Salesforce) and previously held positions at LinkedIn, Greylock Partners, and eBay, where he oversaw initiatives at eBay, PayPal, and Skype. Prior to his work in the private sector, DJ was an American Association for the Advancement of Science (AAAS) science and technology policy fellow for the Department of Defense, where he directed new efforts to bridge computational and social sciences in fields like social network analysis to help anticipate emerging threats to the United States. DJ has authored a number of influential articles and books explaining the important current and potential applications of data science. In 2014, the World Economic Forum named DJ a Young Global Leader. He holds a bachelor’s degree in mathematics from the University of California, San Diego, and a PhD in applied mathematics from the University of Maryland, where he used open datasets published by the National Oceanic and Atmospheric Administration (NOAA) to make major improvements in numerical weather forecasting.
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