My guess is that scientific computation is a lot of fun. Plenty of clean mathematics. Probably not too much dingy boiler room stuff. I can see how one would be attracted to using languages like Python (and maybe Scheme too) for such tasks.
It's huge amounts of glue and connect-this-program-to-that-program-with-a-pile of regexes. Most of the number-crunching's in hand-optimized C or F90...
During my PhD, I designed/wrote a transition-state prediction algorithm in Python, hooking it up to atomistics codes written in Fortran. One of my colleagues, now one of my cofounders at Timetric, wrote a standards-compliant XML library in ANSI F95 - http://uszla.me.uk/FoX/ - which, believe it or not, is one of the rare cases where XML made life a lot better!
That doesn't mean it isn't fun, but it's a lot less clean than you might think.
Building a convincing baseline is hard. Which means that it is difficult to show your approach works in general
Of course, I hand-wrote a lot of my NLP algorithms in Perl, back in the day. Now that's a good use of a time machine...