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Building Data Science Teams

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Building Data Science Teams, 9781449316235 (1449316239), O'Reilly, 2011
Starting in 2008, Jeff Hammerbacher (@hackingdata) and I sat down to share our experiences building the data and analytics groups at Facebook and LinkedIn. In many ways, that meeting was the start of data science as a distinct professional specialization (see “What Makes a Data Scientist?” on page 11 for the story on how we came up with the title “Data Scientist”).

Since then, data science has taken on a life of its own. The hugely positive response to “What Is Data Science?,” a great introduction to the meaning of data science in today’s world, showed that we were at the start of a movement. There are now regular meetups, well-established startups, and even college curricula focusing on data science. As McKinsey’s big data research report and LinkedIn’s data indicates indicates (see Figure 1), data science talent is in high demand.

This increase in the demand for data scientists has been driven by the success of the major Internet companies. Google, Facebook, LinkedIn, and Amazon have all made their marks by using data creatively: not just warehousing data, but turning it into something of value. Whether that value is a search result, a targeted advertisement, or a list of possible acquaintances, data science is producing products that people want and value. And it’s not just Internet companies: Walmart doesn’t produce “data products” as such, but they’re well known for using data to optimize every aspect of their retail operations. Given how important data science has grown, it’s important to think about what data scientists add to an organization, how they fit in, and how to hire and build effective data science teams.
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