I started using python and numpy/scipy back then because it was vastly easier to deploy on a server or supercomputer. The matlab compiler meanwhile is clunky and adds new bugs and additional steps. Julia doesn't really match python in this regard either.
For more pure research and prototyping things both can do, I still think matlab is better though I rarely use it. I just like the idea of being able to easily deploy the code later somehow. Kind of an entrepreneurial feature.
In Julia, there is one package manager and it gets things right. https://docs.julialang.org/en/v1/stdlib/Pkg/ It's super nice to have no fragmentation when it comes to packaging. In Pkg, package states are immutable, always reproducible, and quick. Julia packages that have binary dependencies usually build them all for every platform using the binary builder infrastructure (https://github.com/JuliaPackaging/Yggdrasil). It makes cross platform installation robust and testable, and suuuper quick. Pkg really is the rolls royce of package managers.
For more pure research and prototyping things both can do, I still think matlab is better though I rarely use it. I just like the idea of being able to easily deploy the code later somehow. Kind of an entrepreneurial feature.