On the other hand, the scientific computing crowd for Python/NumPy is well established and there will be plenty of people who have probably come across any problem that you will see. There is already an established ecosystem of tools that you'll be able to draw from, and it's likely that many of them have been well tested and are reliable.
The decision is really up to you. Do you have the time and desire to go on adventure? If so, maybe Julia would be a nice pick. Are you short on time or weary of adventure? Then maybe Python/NumPy would be a better pick.
It doesn't take long to learn Julia well enough to write a Julia package that others will use. For me, that was about 3 weeks @ 10-15 hours a week, and I'm no programmer. It's a small language and it's quite readable. I'd reccomend going through the Julia manual; it doesn't take long and only requires some diligence.
The problem is the lack of packages/libraries and lots of documentation is still missing. That, and the packages that do exist with multiple contributors are still in disagreement about standards and consistency.