I`d use R instead. Python is best used as a glue language to access premade libraries.
R is okay for batch processing, but what happens when management wants engineering to implement data science's models alongside some pytorch models?
Python is totally performant enough if you know how to wield it.
But getting to that point is where the challenge is, and I feel that Python makes thinking about things like data structures and the algorithms you're using (in the case of external libraries) or writing much easier than other languages.
In my experience once you "get there" that is enough.
Storing N-dimensional points in a Point class, for example, is often so slow as to be a non-starter. Admittedly, it's not just a Python problem: struggles with array-of-structs vs. struct-of-array representations are pretty ubiquitous.