Data Scientists at Work

Overview

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 (André Karpištšenko, 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.

What you’ll learn

Readers will learn:

• How the data scientists arrived at their positions and what advice they have for others
• What projects the data scientists work on and the techniques and tools they apply
• How to frame problems that data science can solve
• Where data scientists think the most exciting opportunities lie in the future of data science
• How data scientists add value to their organizations and help people around the world

Who this book is for

The primary readership for this book is general-interest readers interested in this hot new profession and in the nature of the people who work up the readers’ own data trails. The secondary readerships are (a) scientists, mathematicians, and students in feeder disciplines who are interested in scouting the vocational prospects and daily working conditions of data scientists with a view to becoming data scientists themselves, and (b) of business colleagues and managers seeking to understand and collaborate with data scientists to integrate their data management and interpretation capabilities into the competitive intelligence capabilities of the enterprise.

Table of Contents

Chapter 1. Chris Wiggins (The New York Times)

Chapter 2. Caitlin Smallwood (Netflix)

Chapter 3. Yann LeCun (Facebook)

Chapter 4. Erin Shellman (Nordstrom)

Chapter 5. Daniel Tunkelang (LinkedIn)

Chapter 6. John Foreman (MailChimp)

Chapter 7. Roger Ehrenberg (IA Ventures)

Chapter 8. Claudia Perlich (Dstillery)

Chapter 9. Jonathan Lenaghan (PlaceIQ)

Chapter 10. Anna Smith (Rent The Runway)

Chapter 11. Andre Karpistsenko (Planet OS)

Chapter 12. Amy Heineike (Quid)

Chapter 13. Victor Hu (Next Big Sound)

Chapter 14. Kira Radinsky (SalesPredict)

Chapter 15. Eric Jonas (Independent Scientist)

Chapter 16. Jake Porway (DataKind)

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