I suspect there's two factors.

First, I think the current crop of languages with "rich type systems" (I imagine you're thinking of Haskell, Scala, ML and the like?) basically all are in this weird uncanny valley where people get tempted into trying all sorts of cool type tricks to make everything line up, and the type systems are just not _quite_ expressive enough to make it all go well.

Most of the time if you just use arrays/your language's usual collection type of numbers (or arrays of arrays... etc.) in those static languages you'll do great.

To take it to next level and really model ML faithfully I think you probably will need some version of dependent types and there just aren't really any good implementations of that at the moment.

Secondly, a lot of ML is powered by data scientists and statisticians to whom dynamic languages mesh probably better with their own educational background (when was the last time you saw types written out in a stats textbook?). It's really kind of jarring and annoying to have to deal with type systems when you've never done any professional programming before.