Wrong but useful. Working @Twitter, tweeting about data science and people management.
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Wondering how you think about this with the emerging split or rebranding of data science roles as ML Engineering. Do you see that as mostly type B and most advice from SWEng is applicable? Does that put Type A even more on the outs? Love the thoughts already here
I guess it kinda depends on what both are doing. ML engineers are sometimes mostly responsible for the infra for deploying models, sometimes responsible for building the models, and sometimes both. But all those things are more front and center than Type A roles
Often, ML models are directly used to create business value for the customer, and thus they have the same sort of intrinsic value as regular engineering
Type A, be it called analytics or data science or research, is not as immediately obviously valuable. Often what matters most for type A roles, regardless of title, is serving your stakeholders’ needs
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