Thomas H. Davenport and D.J. Patil give the rundown on what a data scientist is, what to look for and how to hire them. It's an article in Harvard Business Review, so it's geared towards managers, and I felt like I was reading a horoscope at times, but there are some interesting tidbits in there. Data scientists don't do well on a short leash. They should have the freedom to experiment and explore possibilities. That said, they need close relationships with the rest of the business. The most important ties for them to forge are with executives in charge of products and services rather than with people overseeing business functions. As the story of Jonathan Goldman illustrates, their greatest opportunity to add value is not in creating reports or presentations for senior executives but in innovating with customer-facing products and processes.
I still call myself a statistician. The main difference between data scientist and statistician seems to be programming skills, but if you're doing statistics without code, I'm not sure what you're doing (other than theory). Update: This recent panel from DataGotham also discusses the data scientist hiring process. [Thanks, Drew]  | | | |
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