Organizational Models for Data Scientists
Published August 25, 2019
23 min
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    When data science is hard, sometimes it’s because the algorithms aren’t converging or the data is messy, and sometimes it’s because of organizational or business issues: the data scientists aren’t positioned correctly to bring value to their organization. Maybe they don’t know what problems to work on, or they build solutions to those problems but nobody uses what they build. A lot of this can be traced back to the way the team is organized, and (relatedly) how it interacts with the rest of the organization, which is what we tackle in this issue. There are lots of options about how to organize your data science team, each of which has strengths and weaknesses, and Pardis Noorzad wrote a great blog post recently that got us talking. Relevant links:
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