What is the difference between an engineering manager and a data science manager? It's a question I find myself ruminating over almost constantly. There's tons of good thinking and writing about eng management out there, but I don't find that it always translates to the DS world.
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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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My suspicion is that most eng management advice can apply to Type B DS management pretty readily. Their work is more purely engineering. But what does that mean for Type A DS management? What makes it different and thus hard to apply the same advice to? I have some thoughts.
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To start, DS is not essential like engineering is. Yes, a product will be worse without analytics on how it's performing, but it can still ship. Everyone loves to talk about being data driven, but many people still operate on intuition and feel.
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It's easy to find ways to a feature launch a success, especially if basic metrics are neutral to positive. Who cares if the differences are statistically significant? We shipped fast! We paved the way for future innovation! And it is fun to use and looks great!
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Replying to @imightbemary
This is definitely not the case in healthcare, although the pressure to ship fast exists the same way.
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Ah, fair point! Having very real-seeming outcomes in the world (like people's health) probably helps mitigate this a lot. Analytics that are tightly linked to revenue are likely similar
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