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.
I think this is dependent on what someone means when they say "data scientist" (mentioned as Type A vs Type B in the article).
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.
I have a research background and do pretty heavy duty machine learning. It’s not analyst work (the internal job family is “applied scientist” while the external facing title is “data scientist”). It’s still not SWE compensation. As far as I’m aware, there is only one team of data scientists that makes the same as SWEs at my company.
Does whatever you build go into production or do you need someone else / team “take care of that part”. That is where the “data science is just Statistics” people’s wheels come off when they realise production ML needs senior software engineering background.
Some of what I've built is currently in prod at a very large scale (which honestly is a bit freaky). Depends on the particular project though. Our team very rarely hands stuff off to SWEs (although they frequently code review); for the most part we implement everything ourselves.
At one of the national labs whose jobs listings I’ve looked at, they have people in ML/data science and ML/data engineering. The first is in the research department and the second is in IT.
I really am surprised to hear this. I'm about 250 all in and I haven't heard of many software devs pulling over 200 all-in. This is for Boston and I'm 6 years out of my undergrad with no graduate degree (although I didn't go to college until my late twenties and I have noticed my maturity helps a little). Maybe on average it's the same but data scientist at the right company has a higher upper limit?
That's interesting that there are data scientists going the other way - you don't really hear about that on the outside.
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?
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.