When I was in college, my understanding was that data scientists do most of the ML related works while there are data analysts and business analysts working on analytics projects. However after coming to industry, I find data scientists in top tech companies work on analytics most of the time, and seledomly have the chance to work on ML. I even don’t know what’s the difference between data scientists and analysts in this sense. And there are applied scientists, MLE, and research scientists (named differently in different companies) purely working on ML. Is it the trend that data scientists are becoming analytics professionals and applied scientists/ML engineers are becoming the “core” data scientists?
TC 200K
Go for research scientist if you really want the hard core ML stuff
Nobody knows what ml.actually is
Best TC is in ML Eng because you're versatile. Good data scientists are rarely good engineers, and good engineers are rarely good data scientists. ML Eng is good at both, so a good one is very rare and will be paid very well.
Applied/ researchscientist or MLE. In one you might get to write papers and in the other you do core ml algo work and implement them at scale - if you are lucky you might get to write your own backprop although rare. Don't do DS.
Job titles aren’t standardized buddy
It's not. Just providing OP some guidance. He/ she might end up LinkedIn stalking to figure out how to get there so title is important to direct the search right
Sadly, titles aren't standardized. Go read a bunch of job descriptions and decide for yourself what you want to be called for each specific company.
Extremely unlikely for research scientist without PhD. Best bet for algo focused role would be applied scientist. Amazon applied scientist AFAIK is pretty respected. MLE imo is hit or miss--some companies just have them implement pipelines with available tooling. Some get to do E2E R&D and prod scaling...
Pretty sure analysts tried to redefine their roles to fit into the DS trend lately, due to higher pay and more demand for these functions. The traditional DS was not an analyst. Most DS roles pivoted over to AS and MLE roles, but MLE is becoming the hotness nowadays due to the democratization of these models and the need to apply them to your business applications and most AS suck at it.
Look at what the role does. Uber DS = Applied Scientist at Amazon. If you read job descriptions carefully, you’ll realize that
Thanks bro. But I think DS at Uber are also doing lots of AB testings for in some teams. Is it true that Applied scientists only work on ML?
Stop associating DS only with ML. You could be doing causal inference full time for a DS job, and that shit is hard yo
What's the first rule of blind? You forgot to respect the first rule.
TC or GTFO