"Vectorizing" (interpreter-out-of-the-way vectorization, which isn't the same and doesn't necessarily give you SIMD vectorization) your code to get Matlab or NumPy to run it efficiently always seems like an unnecessary burden to put on the programmer, especially for algorithms that don't lend themselves to expressing in a vectorized way. Sometimes you just need to write a for loop, and it's great when the language gets out of your way and lets you do so without slowing your code down by an order of magnitude.

If you care about constrained optimization, Julia has leaps-and-bounds more sophisticated tools than anything Matlab or Python have to offer. Check out http://www.juliaopt.org and especially JuMP.jl. http://www.optimization-online.org/DB_FILE/2015/04/4891.pdf has some detailed comparisons. Macros and fast generic programming make Julia a very well-suited language for doing automatic differentiation (https://en.wikipedia.org/wiki/Automatic_differentiation).