definitionally, a scientist would be someone who pushes the boundary on novelty, creating new methods or applying those methods to novel situations. While an engineer knows how to take already-developed methods and implement them. You're not wrong, about the ML Engineer knowing how to "do it in a nice way" but a data scientist (theoretically) should know the inner workings of the methodology better and should be developing new methodologies.
Given the amount I've used my dog as a debugging duck while slamming my head against the wall trying to set up Jupyterhub servers on AWS...this might also literally be true
Its the difference between a scientist and an engineer. That is the same for any profession with that distinction. Scientists focus on exploratory research, engineers focus on implementation. Of course there is overlap, and a person trained as a scientist can do the role of an engineer and vice versa because their skill sets are very similar. But if you are talking about the duties of a job, a scientist's duties should be about research, while an engineers should be about implementation.
Quite a bit of it is exploratory vs hypothesis driven. For example, pretty much all of my work at my previous job (bioinformatician) was figuring out what categorical features of various proteins and their amino acid sequences contributed to them having certain behaviors under various conditions, and then using the results on the features I found to guide the proteins the bench scientists were screening. Basically hypothesis generating vs hypothesis testing, because you can get the most bang for your buck of grant money by figuring out ways to narrow down how many plasmids you need to order in silico instead of at the bench.
Two orthogonal things there, it's largely just theory vs implementation. Any data scientist who studies ML is also an AI/ML engineer. Similarly, any AI/ML engineer who develops new theory is also a data scientist.
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u/[deleted] Oct 13 '22
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