Thanks for the info. What worries me is that you can replace 'Julia' with 'Scala' in that sentence and a data scientist wouldn't know the difference. It's already fast. If Julia wants to win in data science then they need to poach users away from other languages.
As much as many of us would like it to be, the kind of data science work you see Scala used for is a pretty small part of what Julia is used for.
I think a big part of that is because DS rarely involves writing fast numeric kernels or hot inner loops, i.e. user code that needs to do numeric stuff quickly. This is in large part because very large organizations have poured untold millions into libraries that already handle this (e.g Spark).
In domains where this has not happened or that have more bespoke requirements (e.g. modelling and simulation), something like Julia is far more compelling. That's not to say it's not viable, but unless more practitioners start feeling stuck in a rut [1] I don't see the mindshare changing dramatically.
