The Clojure/JVM statistics/scientific computing/now tensor math packages just never got as good as Python, and in Smalltalk they were a non-starter. R is an awkward language, but it's repl-first, has a lot of Lisp's metaprogramming (done in very ad-hoc ways), and Smalltalk's serializable image model -- so a lot of exploratory/experimental statistical methods research happens there, and then gradually makes its way to Python when it needs to be stabilized for production.

F# based .Net would've made a fine math-centric environment, but it's a language with a high initial barrier, and though .Net community has been making a decent effort at porting over a lot of NumPy/SciPy, it's not fully caught up after many years.

We have Julia now, it's jitted, multicore/GPU friendly, and has interesting REPL innovations, and yes, serializable state (though, ugh, ligatures). But every time I reach for it, it's like, oh no, yet another thing I want to call is in the Python ecosystem. Especially all the modern deep learning/tensor stuff, where the assumption is Python's speed and the GIL don't matter because you're just gluing together GPU calls.