for many types of scientific computing, there's a case to be made it is the best language available. often this type of computing would be in scientific/engineering organizations and not in most software companies. this is its best niche, an important one, but not visible to people with SWE jobs making most software.

it can be used for deep learning but you probably shouldn't, currently, except as a small piece of a large problem where you want Julia for other reasons (e.g. scientific machine learning). They do keep improving this and it will probably be great eventually.

i don't know what the experience is like using it for traditional data science tasks. the plotting libraries are actually pretty nicely designed and no longer have horrible compilation delays.

people who like type systems tend to dislike Julia's type system.

they still have the problem of important packages being maintained by PhD students who graduate and disappear.

as a language it promises a lot and mostly delivers, but those compromises where it can't deliver can be really frustrating. this also produces a social dynamic of disillusioned former true believers.