Have been thinking changing jobs recently. For future development, which one has more potential, DS or MLE?
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MLEs are the manual labor for implementing the DS’s insights at scale. Important, sure, but theoretically a little more proletarian
As far as MLE, I'm not sure what your org considers MLE. But every MLE has to know all the topics you listed. I've worked at FAIR and also collaborated with AML folks before and we had some MLE who wrote papers and collaborate well with scientists.
That said, if you're sitting on the sidelines and not actively doing the job, it's feasible (though still hard) to keep up with most major innovations in DL as a whole. Only a handful of papers actually propose something that's both feasible and innovative.
MLE: A person who can conceptualize, build and train ML models. Can put them into production etc..
DS: Person with a more general statistical domain knowledge. Can probably prototype ML models if ML is the focus, but typically not capable of putting into production. But may be focused on other fields like time series forecasting, causal inference etc..
Which is better? Depends. MLE will certainly pay more medium term. Long term, DS is little more business focused and reaching senior positions will be slightly easier. What's your objective?
If you really only care about machine learning, be an MLE. As a data scientist you're going to be expected to solve all types of data problems. Nobody cares how you do it. As an MLE people expect a specific set of skills.