Still, besides that issue - which all novice programmers have, but Data Scientists come in at a much more senior level - it was a great market advantage, one that I think is largely gone today, as companies understand the need for statisticians and economists who really and truly understand modeling and math.
Anecdotally, I've worked in places where this is a major source of contention - The Data Scientists treated as talented individuals (who produce broken solutions which work for cherry picked data sets), and anyone else is just a monkey who maintains and fixes the broken code.
This is very much the opposite of my experience.
There was a company that did this a while back, I don't even think they actually had a product, they just knew that one of the big four would pay millions to acquihire them.
The go-to graduate level machine learning text "The Elements of Statistical Learning" was written by 2 statistics professors (one, Prof. Hastie, a fellow South African! :)
Granted, neural networks are often taught as a bolt-on lecture in statistics course machine learning modules but topics like regularization and a rigorous study of overfitting were born in stats departments.
That being said, there are few of them and they congregate to the big tech and finance names. It can look like everyone or noone has a formal education, depending on where you personally work.