The "iterate from notebook to production" process which is common everywhere but the largest data engineering groups rules out anything with manual memory management from becoming popular with data science work.
Some data scientists I know like (or even love) Scala, but that tends to blow up once it's handed over to the data engineers as Scala supports too many paradigms and just a couple DSs will probably manage to find all of them in one program.
We use Go extensively for other things, and most data scientists I've worked with sketching ideas in Go liked it a lot, but the library support just isn't there, and it's not really a priority for any of the big players who are all committed to Python wrapper + C/C++/GPU core, or stock Java stacks. (The performance also isn't quite there yet compared to the top C and C++ libraries, but it's improving.)
I love scala and wish it was more popular. I've made piece with java at this point as it slowly adopts my favorite parts of scala but I miss how concise my code was.
Some data scientists I know like (or even love) Scala, but that tends to blow up once it's handed over to the data engineers as Scala supports too many paradigms and just a couple DSs will probably manage to find all of them in one program.
We use Go extensively for other things, and most data scientists I've worked with sketching ideas in Go liked it a lot, but the library support just isn't there, and it's not really a priority for any of the big players who are all committed to Python wrapper + C/C++/GPU core, or stock Java stacks. (The performance also isn't quite there yet compared to the top C and C++ libraries, but it's improving.)