Python is pretty much established as the leading open-source foundation for high-level scientific computing, competing head-on with tools like Matlab and IDL, either via the pure 'python stack' (Numpy, Scipy, Matplotlib, ipython - http://www.scipy.org/ and tools around them) or a project like Sage (http://sagemath.org).
I suggest you find a *topic* that interests you, that you're likely to work on for fun. If it's something that can benefit your research, even better. Then try to improve the specific package that covers that problem. Python is a much easier language to get into than C++, yet there are ways (with Cython and C/C++/Fortran) of getting performance when needed.
The range of topics where significant contributions can be made ranges from the very low-level, hard-core optimization work to high level user interface and visualization libraries. Special functions, ODE integrators, statistics, code generators, visualization, you name it, there's work to be done and welcoming communities in Python. If you'd like more specific pointers, drop an email to the Numpy discussion list as a starting point, indicating with a bit more precision what topics you find interesting intellectually. You'll find a welcoming reception and guidance on where to go from there, until you can find a project to focus your energy on.