Why are you restricting the discussion just to data science? In "general" science there are way more devs than just in data science / statistics, and Julia absolutely shines there. Don't get me wrong, the language is general purpose, the ecosystem is a bit niche for now, but still, it seems wild to restrict comments to such a small field as data science.
I'm a mathematician at a research university, and maybe two of my colleagues are using Julia. Despite their proselytism, everyone else is using Python, C, or math-specific software such as GAP, Matlab, or Mathematica.
Depends on what exactly you would like evidence for. Your dissenting comment is that Julia is not popular yet. With that I can easily agree, but that is also not directly related to whether it is an amazing tool, which was my claim.
In terms of examples of hard sciences where it shines: It is the only tool in existence that has at the same time high-quality differential equation solvers and autodiff on them. Compare DifferentialEquations.jl to any other package in any other language. The rich capabilities of the aforementioned package depend on the multiple dispatch + aggressive devirtualization used in Julia. Python/Jax/Tensorflow/Pytorch while wonderful on their own, are nowhere near these capabilities. Matlab/Mathematica do not have these capabilities. The famous C/Fortran/C++ libraries are also far less capable in comparison.
