I think it depends on the size of your organization/team, since for smaller organisations you would need to manipulate your variables and input data much more than larger ones, and therefore would need something more nimble like Python. More enterprise level, which generally have predefined data tables and teams/tools, teams would prefer you work with C++ or Java as a programmer, with the rare C#
I prefer to work Python which, although is definitely not the fastest language, it is the most portable and allows more dynamic manipulation/preprocessing/fetching, and most well-defined mathematical optimisation problems have been implemented with Python APIs. Plus Jupyter Notebooks allows for very visual and iterative coding for analytical work.
However, you would be hard-pressed to find specific optimisation scenarios and would likely require you to implement that logic. For example, you can execute a number of Mean-Variance optimisations by configuring CVXOPT solvers (http://cvxopt.org/userguide/index.html) in Python, such as creating a hedged portfolio, but you would not find readily available (and free) 'Selective Hedging' optimisations, pre-implemented outside of GAMS.