Career advice - DS vs. ML Career Paths
Currently work at SAP as a data scientist. Although the title is that of a data scientist, I hardly do any of the ‘data science’ work (experimental design, A/B testing, statistical analysis) in the sense of other tech companies. It’s mostly some rough prototyping of ML algorithms in R/python for different problem domains like churn analysis, image recognition, supply chain analytics, nlp etc. It is not any fancy novel research but mostly applied research to solve the incoming problem in the quickest way. I’m currently torn between choices on the skills that I should specialize in to be successful in the long run. Please recommend ideas on what I should do, if there are any other possible paths then please do bring them up for discussion. Thanks a ton! TC 135k, YOE 5 (2 in current field)
Sounds like Burlington MA
You need to move from SAP ASAP. I was there too at some point. That experience didn't contribute much to my future career.
Statistics is a requirement for either of your first two options. Don’t get a PhD though unless you specifically need it for the job that you want to have (e.g., academia, research science, applied science).
I agree. Thanks for your response, I was wondering what the differences are in terms of expectations from these different kinds of roles specific to Amazon - 1. Applied Scientist 2. Machine Learning Scientist 3. Data Scientist
Just found the answer to my question in a separate blind thread. Posting here for everyone’s benefit- Amazon has Data Scientist - basic ML with scripting Research scientist - statistician who knows how to code a bit Applied scientist - coder who knows ML Applied scientist > research scientist > data scientist
If you want to do legit research PhD. My guess is that for a non PhD the ml engineer path might be more lucrative. The work might not be that cool though.