Why Data Science Teams Need Generalists, Not Specialists


This post takes an important stance: it's arguing that hiring a team of generalist data scientists is better than hiring a team of specialists. The author, head of algorithms at Stitch Fix and previously of Netflix, is obviously very qualified to make this case.

In the post, though, I think he alludes to one of the biggest arguments against it:

Finally, the full-stack data science model relies on the assumption of great people. They are not unicorns; they can be found as well as made. But they are in high demand and it will require competitive compensation, strong company values, and interesting work to attract and retain them. Be sure your company culture can support this.

First, there are a fairly small number of companies who can provide this set of things. Second, and more importantly, making a generalist data scientist is quite hard: it requires lots of time and support from an experienced team. And in the process of becoming a generalist, it often makes sense to do "tours of duty" as a specialist in various areas.

I'd reframe this piece by saying that as an individual data scientist, your goal should be to evolve into a generalist over time. You will likely command a higher salary, will have more fulfilling experiences, etc. But if you're building a team of data scientists, you'll probably need to hire a diverse set of folks. Unless you're leading a team at Stitch Fix or Netflix...then you can make decisions largely free of constraints 😉


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