However they claim using Haskell for data science is "fast", which doesn't really mean anything until you have numbers to show. A little benchmark with pandas and polars wouldn't hurt I guess.
It's been one of the plays that the F# community has also been trying to make (somewhat with a modicum of help from Microsoft's marketing arm, but not enough help from what I've seen) pitching F# as a language close enough to Python to feel familiar and useful to data science but with the performance help of rich ML types and the modern .NET performance ecosystem.
(To my experience: getting "fast" compared to Python seems easy for most functional languages. Getting data science out of Python seems hard for a lot of sociology reasons more than technical ones.)
Unoptimised/naive Haskell might be that slow. But that's true of a lot of languages and isn't particularly interesting to me. Java is slow if you do everything the naive way, too.
[0] https://entropicthoughts.com/on-competing-with-c-using-haske...
Java's most badly optimised type, is the String.
Both of them need a string-builder pattern, the default operators don't work around things to do the right thing for you. They expect you to understand how data works.