From DS Analytics (like those in FB), how hard is it to switch careers to DS/ML engineering? Data Science is a broad term, so I want to know how these two sub-categories of career paths are related. If you are an Analytics Data Scientist who understand ML models well, and do a lot of leetcode to be able to pass technical screens, then is it pretty easy to be considered for ML roles? DS Analytics still signals you have a solid statistics background, right? Has anyone made this transition before? What was that like? What I mean by: - DS analytics: you do a lot of experiments and maybe even interact with ML engineers to give input on what models are useful. Heavy on SQL and business intuition. - DS/ML engineering: you productionize ML models, but you are not a research scientist. Heavy on programming and ML modeling. #data #datascience #machinelearning #careers data analyst at Uber. yoe 6 tc 150
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There are in between generalist jobs too, might be easier than applying to straight to research scientist. Pad the resume with having read some ML papers and implemented from them