What's happening with data scientists?

The most recent issue [$] of The Diff discusses this interviewing.io analysis of recent layoffs.

One finding Byrne discusses is that data scientists are being laid off at surprisingly high rates (much higher than software engineers). Byrne suggests it is because staffing data science tends to be associated with expansion. Here are some other hypotheses:

  1. At many companies, data science is used for exploratory or speculative ends. Stopping such projects can be less painful than stopping other projects--even if it's a mistake to do so. (And in some cases, stopping those projects puts more decisionmaking power with the people doing the layoffs.)
  2. Anecdotal: some people who would have called themselves "data scientists" five years ago are now (maybe) calling themselves "ML engineers" or similar. It would be interesting to investigate whether and how the status and seniority of the "data scientist" title is changing. In the Stable Diffusion Era, I'd guess that people who work directly with ML models are even more eager to have "ML" or "AI" in their job titles.
  3. Relatedly, some tasks in the traditional domain of data science are now being done in ways that look a lot more like software engineering (or the automatic behavior of software). This probably goes both ways, with data science capturing some other territory, but I'd guess that there's a net loss here. (This intersects with the point above about prestige.)
  4. Computing power is more usable than ever (with better cloud services, M1 Macs, etc.). In my experience, this accelerates data science more than it does other kinds of software work. A lot of data scientists spend significant time waiting for calculations to finish.

Those are all hypotheses I'd want to investigate; they are certainly not firm beliefs.

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