Didn't see anything for Haskell so I'll do that. Good link in general would be https://www.linkedin.com/pulse/haskell-data-science-good-bad-ugly-tom-hutchins ht

Didn't see anything for Haskell so I'll do that. Good link in general would be https://www.linkedin.com/pulse/haskell-data-science-good-bad-ugly-tom-hutchins https://www.linkedin.com/pulse/haskell-data-science-good-bad... - Python is established for Data Science vs Haskell You could use Haskell for whatever models you need (although finding 10-20 people for that might be a challenge) and it would be really easy to refactor for any changes you might have but I dunno if data science is something you'd use Haskell for. Most data science applications I can think of are either scripts to inspect/clean data (Python works well) and the rest involve a lot of trial and error (Haskell errors aren't great). - Haskell libraries are awesome You could use Haskell for whatever important libraries you use on a regular basis - it's incredibly easy to write APIs for different languages in Haskell (which is great if you switch languages for some particular use) and it's easy to refactor (which is important in a new company that's likely to change a lot). Also, it's a lot harder to write shitty core libraries in Haskell. I'd honestly recommend sticking to whatever language you already have code in. If you already have python models built, stick to them!