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Data Scientists at Work is a collection of interviews with sixteen of the world's most influential and innovative data scientists from across the spectrum of this hot new profession. Data scientist is "the sexiest job in the 21st century" and data scientist job openings increased 15,000 percent in 2012 over 2011, according to the Harvard Business Review . Through incisive in-depth interviews, a data entrepreneur explores the what, how, and why of the practice of data science as revealed in its practitioners stories, ideas, and aspirations. Data scientists in the diverse sectors of finance, venture capital, angel investment, retail, e-commerce, fashion, government, education, science, mathematics, politics, news,and sports share with Sebastian Gutierrez how they tailor the torrent-taming techniques of big data, data visualization, search, and statistics to specific jobs by applying intellectual ingenuity, statistical skill, and visual imagination.§The backgrounds recounted by the data scientists interviewed in this book are as diverse as their current practices of data science. Jeff Hammerbacher, who coined the term data scientist, went from being a Wall Street quant to using Hadoop to exploit Facebook s massive datasets in real time for targeted advertising, to founding Cloudera, where he applies Hadoop to oil and gas exploration, retail, and the life sciences. Amy Heineike, the Director of Mathematics at Quid, got started in network analysis of the effects of social media on public policy. Nate Silver segued from the statistics of fantasy baseball to the uncanny forecast of elections. Anna Smith shifted from the astrophysics of cosmic evolution to crunching social media data at bit.ly in pursuit of zombie trends. Hal Varian, the Chief Economist at Google, mutated from microeconomic modeling to designing online advertising auctions. Mike Bostock transitioned smoothly from authoring D3.js, an open-source JavaScript library for data visualization, to designing the interactive graphics of the New York Times .§§The data scientists in this book speak freely of the day-to-day travails and pleasures of extracting actionable meaning and competitive intelligence from rich but recalcitrant big data sets. Gutierrez parts the curtain on his interviewees earliest data projects, how they became data scientists, their discoveries and surprises in working with data, their thoughts on the past, present and future of the profession, their experiences of team collaboration within the organizations to which they contribute, and the deep insights they have developed as they get their hands dirty refining mountains of raw data into objects of commercial, scientific, and educational value for their organizations and clients.§
