At work, we make high performant drone control systems in Rust. But for testing and debugging, we simply use Matplotlib (a Python library) to make plots of simulations, etc. So we prefer Rust for the control systems themselves for the speed and reliability, but for plotting results of simulations, we don't care much about performance and rather just use something with all the features we need.

90% of time is spent in 10% of code(for some value of 90/10). As a concept, this will give you freedom to use extensive Python ecosystem, flexibility to write glue code etc. in a more 'accessible' language.

Then I guess you're not looking hard enough. Recently swift integration with tensorflow also introduced easy way to import python. There into swift. Data science engineers need performant language to do lot of stuff, and rust fits the ball. But it's hard to abandon python ecosystem.

I and others hate the pattern in python to write anything performant in C, Cython or some other language and write bindings for python. The python as binding language paradigmn doesn't work when all the stuff you want don't exist in performant languages.

TLDR; this fits the perfect use case for machine learning/data science, to use python only when necessary and hopefully migrate projects (parts of them atleast) to rust.