Definitely, since Julia has the same approach as Python of allowing quick and dirty solutions for data analysis/modelling, but with a much larger scope even when you don't have complete library support. In a few hours you can make a functional pytorch clone (and just using special GPU arrays you get it running on GPUs) with similar performance [1], and within a day (given a very good understanding of the language) a method that compiles the gradient directly from unmodified Julia code [2]. Plus native matlab-like goodness such as multi-dimensional arrays, so you don't have a separate library for fast operations and you can just use normal loops or whatever you want.
But while Julia targets Python fast and concise (while not compromising speed or power), it does not target the slower but more correct (though there is a culture of testing, which is quite important for math-oriented problems since the type system will not catch the most subtle and troublesome problems). There is space for a language to do exploratory/research work that can be quickly deployed in a fast iterative cycle and another for the new Spark/Flink or critical production areas that needs to take the extra effort (like self-driving cars), which could be Rust (or Scala, or Haskell, or Swift or stay with C++/Fortran).
