Python is a good choice for data science even at relatively large scale. I'd question it's suitability for stable, scalable deployment in production (not to say it can't be used then, just that I wouldn't necessarily reach for it first, preferring either C++ or Rust for that).
Scala just doesn't figure into the picture at all. I consider that some "Big" data tools were written in it to be a matter of trivia and not essential to the work of data science.
I think for a particular data science mindset (the category theory toting, bijection loving person) Scala actually _is_ essential to the work of data science. But these people are in a minority.
Anyway, if you're truly in the second category, then the fact that the best library for doing X is in Scala would mean you're going to write some Scala, despite the fact that it's ~accidental that it was written in that vs Python.
It's not even clear to me that most companies doing data science in Scala have that scale -- they're just using tools and libraries companies at that scale have open sourced. You could call it cargo culting, but I think it's more nuanced than that. I think engineers can be separated into two camps roughly: those who are passionate about the language(s) they use, and those who are simply trying to get the result they need and don't care what language they use to get it. A lot of data science engineers naturally fall into the second bucket, so using Scala because a library they want to use is written in it comes naturally, even if they could get the job done in Python (possibly with a bit more wheel reinvention).