Statistics expertise is also very important. Stats may be less sexy today than "data science", but they were doing "model explainability" before it was even a thing!
I always took data scientist to be a rebranding of statistician. (To be clear there's nothing wrong with rebranding.)
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.
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.
Do you have an example?
But more classically, any structured statistical model with (eg) terms for variable interactions, measurement noise, and hierarchy.