Granted, "Data Science" is still a broad cover term, so depending on your flavor of data science, the leap is shorter. One way of segmenting the DS world that relate to is the type A vs. type B DS, where A is for "analysis" and B is for "building").https://medium.com/@rchang/my-two-year-journey-as-a-data-scientist-at-twitter-f0c13298aee6 …
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As a type A DS manager, it makes you wonder if you're really managing a team in the same way that an engineering manager is managing a team. There's less of a shared identity. It's hard to say what you're doing as a TEAM, rather than as individuals.
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Now, this all might sound like a lament, but I'm not necessarily saying that it's a bad thing. Type A DS teams are viewed as important (maybe even essential) partners much of the time, and an embedded, verticalized team structure usually generates good outcomes for a company.
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It's not bad, but it is different. There are some days where I what the hell my job as a DS manager is even supposed to be, but thinking through the differences helps bring me some clarity.
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This thread is awesome! A Q on this tweet – do you mean that type A DS can't collaborate, or shouldn't collaborate? I'm biased, but seems to me that part of the reason for siloing (assuming everyone is working in adjacent areas) is a lack of proper tooling...
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Even if you want to manage people collaboratively you hit a lot of bumpers (in the best case, or walls in the worst) making it harder to share data, artifacts, approaches and results
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