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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Replying to @imightbemary
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
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Replying to @data_mesh_learn
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
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Replying to @imightbemary @data_mesh_learn
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
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Replying to @imightbemary @data_mesh_learn
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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Replying to @imightbemary @data_mesh_learn
Stakeholder centricity means that perception of service quality is really important. It can even outweigh actual quality of service
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Replying to @imightbemary @data_mesh_learn
Managing this type of group is a big mindset shift if you only hear standard EM advice. You need to pay attention not only to your relationships with your stakeholders, but also your ICs’ relationship with your stakeholders
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I haven’t finished reading it yet, but Managing the Professional Service Firm by David Maister is one of the best books I’ve found on this subject. I would recommend checking it out based on what I’ve seen so far
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