I don't understand what it can possibly be without statistics expertise. If you take away that, what's left? a A not-very-good programmer?

I always took data scientist to be a rebranding of statistician. (To be clear there's nothing wrong with rebranding.)

Well the common description of a data scientist as "better at stats than a programmer and better at programming than a statistician" can also be reframed as "worse at stats than a statistician and worse at programming than a programmer" in most cases. I think it's typically a rebranding of PhD in a science field realized that market for that educational track is very small so rebranded as "hottest job of the next millennium"

I wasn’t familiar with that formulation. Are contemporary stats grads really any weaker in programming than data science certificate holders?

I can buy that there’s a shortage of statisticians and so industry needs other people to pitch in, but it seems like the floodwater of incoming students should be directed to stats programs and not data science ones.

Statistics is boring and uncool.

ML is hip and profitable.

Hoenstly, it's mostly garbage. The original notion of data scientists was invented by FB for a very specific set of skills (social science PhD's with Map-Reduce and experimental design), but it's a cool title and thus it got spread across multiple roles.

It's super weird though, despite doing data sciencey work for about a decade now, when I changed my title on LinkedIn to be data scientist, I started getting offers for jobs that paid a lot more money, so there's an incentive on the candidate side to re-brand.

But yeah, ultimately all the job is is some stats, some code, and some communication. Don't get me wrong, its a great job and its hard to find people who are good at all of this, but in my experience the limiting factor is definitely the statistics and the domain knowledge rather than the code.

>"but they were doing "model explainability" before it was even a thing!"

Do you have an example?

To an extent, CART.

But more classically, any structured statistical model with (eg) terms for variable interactions, measurement noise, and hierarchy.

Prior to model explainability was experimental design.

Linear Regression

Check this: https://news.ycombinator.com/item?id=19353204