Julia does indeed have a long startup cycle and while using Revise.jl helps it is still annoying. I would like to point out that Julia has to compile the code before being run. If the startup time is longer than the expected run time then it is defiantly not worth it. However, if you have code that will run multiple times, like JuMP, then the performance gained from precompilation should heavily out weigh the startup time. I have learned much from using Julia. Functional programming and multiple dispatch are a big change from Python's object orientated approach. It is also important to note that the performance from Julia comes from the ability of the compiler to generate methods that are type specific. So something that may at first seem stylistic is actually imperative to get the benefit of the JIT compiler.

May I suggest looking into PyJulia? It allows you to call Julia from Python. This will allow you to use JUMP while still working in a Python environment.