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," according to the Harvard
Business Review. By 2018, the United States will experience a
shortage of 190,000 skilled data scientists, according to a
McKinsey report. Through incisive in-depth interviews, this book
mines the what, how, and why of the practice of data science from
the stories, ideas, shop talk, and forecasts of its preeminent
practitioners across diverse industries: social network (Yann
LeCun, Facebook); professional network (Daniel Tunkelang,
LinkedIn); venture capital (Roger Ehrenberg, IA Ventures);
enterprise cloud computing and neuroscience (Eric Jonas, formerly
Salesforce.com); newspaper and media (Chris Wiggins, The New York
Times); streaming television (Caitlin Smallwood, Netflix); music
forecast (Victor Hu, Next Big Sound); strategic intelligence (Amy
Heineike, Quid); environmental big data (Andre Karpis ts enko,
Planet OS); geospatial marketing intelligence (Jonathan Lenaghan,
PlaceIQ); advertising (Claudia Perlich, Dstillery); fashion
e-commerce (Anna Smith, Rent the Runway); specialty retail (Erin
Shellman, Nordstrom); email marketing (John Foreman, MailChimp);
predictive sales intelligence (Kira Radinsky, SalesPredict); and
humanitarian nonprofit (Jake Porway, DataKind). The book features a
stimulating foreword by Google's Director of Research, Peter
Norvig. Each of these data scientists shares how he or she tailors
the torrent-taming techniques of big data, data visualization,
search, and statistics to specific jobs by dint of ingenuity,
imagination, patience, and passion. Data Scientists at Work parts
the curtain on the 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 their
organizations, and the insights they have gained as they get their
hands dirty refining mountains of raw data into objects of
commercial, scientific, and educational value for their
organizations and clients.
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