I suggest gaining more experience by joining or creating a friendly open-source community (focused on scientific computing, e.g., vtk.org, itk.org, slicer.org, paraview.org) and learning the ropes. Or as you say joining a research organization in the capacity as a junior programmer, or graduate student. Software skills are important but what might be more important is technologies, since programming languages come and go quickly. For example, understand how to basic 3D graphics works, and then study implementations on GPUs. Or learn algorithms for high-performance computing and then study tools like MPI or Boost to understand implementations.

It's strange that this article focused on Python and Continuum when there is a much bigger story to be had. The XDATA program is being run in a very open source manner, and there will be a multitude of open source tools created and delivered by the end of the contract. The program is focusing on two major tasks: the analytics/algorithmic tools to process big data; and the visualization/interaction tools that go along with them.

I recommend that you visit this course wiki. It contains free material relevant to participating in an open source projects. The course, "Open Source Software Practice" is a taught at RPI. http://public.kitware.com/OpenSourceSoftwarePractice

Complete nonsense: ParaView, ITK, CMake, CDash, Slicer, Titan, MIDAS, vxl, IGSTK, and more: all open source, some are toolkits, some are applications. These tools are in widespread use in production environments. The company teaches an open source course at RPI, and particpates in things like OSCON, etc.

Look into VTK and ParaView. Both open source packages (BSD license) from kitware (vtk.org and paraview.org). Historically they have been a scientific visualization system, but in the recent two years they have worked with Sandia Nat'l Labs to add an extensive information visualization subsystem. They major goal of the work is to make it scalable (using distributed parallel processing); something that's really important as data sets get large.