Earlier quoted context omitted.
Not sure what boilerplate in Rust or Scala you're talking about. I admit Rust is much harder to learn initially, but once learned properly, the amount of code in both languages is very similar (as long as we're not comparing one program calling out to a library and another one doing everything from scratch). Both can be very high level. Here is a study where they've found Python to be not much less verbose than Java…
That study is also based off of Rosetta code tasks. I'm not sure if that accurately portrays code in the wild. Java codebases are full of design patterns that are entirely uneccessary in Python. Ever hear someone talk about design patterns for Python? It exists, but it is niche instead of the norm and I've literally never heard it mentioned in hundreds of hours talking about Python with coworkers. I'm sure Rust makes…
So the differences you observed might be not because of a language itself, but the complexity of projects these languages are applied to and cultural differences of the teams. So far I haven't worked on Python projects as big (in terms of functionality) as Java projects I've seen.
As for data science, so far I haven't stumbled upon any Python code that wouldn't look very similar translated to Java, Scala, R or Rust, assuming same libraries existed. Most of the code is very simple really: load data into some vector/matrix, apply some library code on it, get a different vector/matrix back, etc. The only thing that holds me to Python really are libraries.
As for concurrency - gluing systems together sometimes needs concurrency to cut the latency down. And in data science parallelism also means performance, and often it is needed. I'm not that convinced Python is a clear winner here.