From my perspective Julia is sacrifising some amount of "duct tape UX" to gain speed, and that's the wrong direction.
Whenever we need more speed, we just pull the slow bits down into compiled languages, and scale them out to many cores with solutions like MPI.
R is another language that is mainly duct tape for stringing pieces of compiled code together. If it had a better UX for developers than Python, I think we would see it dominating much more today, without having any speed advantage.
Interestingly, R is probably a better UX for statisticians/data scientists than Python is (almost all the good parts of Numpy/Pandas were in R first), but it really suffers from not being well known by developers.
To be fair to R though, it's much, much easier to deploy than Python, which is a shocking indictment of the current Python packaging ecosystem.
This only works sometimes--for problems that allow you to do a relatively large amount of computation in the compiled language to justify the cost of marshalling Python data structures into native data structures. For matrices of scalar values, this works well. For many other problems (consider large graphs of arbitrarily-typed Python objects, or even a dataframe on which you need to invoke a Python callback on each element). If you rewrite a big enough piece of your Python codebase in the compiled language, then it will work, but now you're maintaining a significant C/C++/etc code base and the bindings and the build/packaging system that knows how to integrate the two on all of your target platforms. Python really doesn't have a good answer for these kinds of problems, and these are by far the more common case (though perhaps not more common in data science specifically).
What particular language features of Julia make that trade-off? (Not a rhetorical question, I'm not disagreeing with you, just curious; I'm familiar with Python, not really familiar with Julia.)
These aren't fundamental issues with the language and will be solved by tiered compilation (there's already an interpreter mode, just have to integrate that with normal use), separate compilation and incremental sysimage creation which works with the package manager.
Some of Python's strengths that I work with regularly are dynamism, easy data exploration, visualizations, succinct & customizable syntax, extremely strong data science libraries, REPL/Jupyter, C bindings, easy to use packaging solution via PyPi (bet some people are going to disagree with that one), quick to prototype code. If you really need performance in Python, you can probably get it out of the box (if you can run deep learning with Python, performance isn't a limitation).
I love Go and use it for a bunch of projects, but I've only once wanted to move a project from Python to Go and that was a performance-centric CLI that was only written in Python originally because of how quickly it let us prototype in comparison to Go.