[FacelessJim]

You could take a stab at Julia.

It’s still dynamic in nature. But you can tune how much staticity you want. The spectrum goes from Python to C in terms of staticity. And with tools like JETLS.jl maturing you get a lot of the benefits static analysis.

The data pipeline ecosystem is starting to rival that of R and Python. The fact that you can just use Julia functions while keeping the performance allows you to avoid those weird vectorization gymnastics. The ML ecosystem is also in a great state. JUMP.jl, Touring.jl, the whole SciML ecosystem, autodiffing and gpu computing are all close to best in class in terms of quality. The NN side of ML is a a bit weaker, but just for lack of developer time investment into that side of the ecosystem.

[SatvikBeri]

I use Julia! I like it a lot, and add type parameters to all application code. But JET.jl does not feel anywhere close to the assurances I can get from a statically typed language (yet)