That’s interesting. I’m at one of F/G and a lot of the data scientists want to go the other direction to software engineering because we receive about 60% of the RSUs that they do. A few people on my team actually did switch; they said they found the data science work more interesting but an additional $40-100k per year can make a really big difference over the long run.
Facebook and Google "data scientists" (meaning those who hold the title) are really more like analysts -- they analyze data to inform decisions and use a lot of SQL. They make prototype models (usually based on less cutting-edge techniques) that get passed to engineering teams if they become worthwhile to scale/formalize. These folks get paid less than SDEs usually.
The other type of "data scientist" is basically an SDE (maybe SDE-lite) with research-level ML skills. These get paid similarly (or higher in some cases) than SDEs. I believe Facebook and Google call these SDEs. Sometimes the term "applied scientist" is used to describe these at other companies as well.
At my company, Type A are called "Data Analysts", but at Google Facebook they're called "Data Scientists". Type B are "Data Scientists" at my company, but "Machine Learning Engineers" (or SWE-ML or some other combination) at Google and Facebook.
As a Type B, at my company, I'm on the same pay scale as the SWEs. The Type As are not.
What sorts of SDE positions do these data scientists go into? Are there any additional skills they pick up as part of the transition, or are strong Python/SQL skills enough?