So, although lisp might have been great, in the end I stayed with Python/C++. Guess I'm not the only one with similar experiences.
Thankfully, however, one can tweak it in so astonishing a way - I still miss parameterized types though - that once you realize the nature of your problem, and put in lots of due work, things tend to become easy in the long run; I particularly adore being able to write iterate macros (adding new "for" clauses").
That said, Matlisp is still in a stage of infancy ("research grade"), and while I welcome contributions, it should be noted that it is not yet a replacement for R/Numpy/MATLAB, atleast not for the casual user.
What we use: C/C++, Matlab, Python, R, Fortran, and the occasional Mathematica. I myself have been getting into Julia.
Although written in C++, it uses MIT-Scheme/guile as the control language. This is a problem because the steep learning curve discourages undergraduate students.
https://en.wikipedia.org/wiki/Learning_curve
MEEP looks interesting. Filed, for future use.
The key point is that if you were to map out different languages along the abstraction level, Lisp sits right in between the realms of low-level programming (e.g. Java) and modelling (e.g. Matlab). [2]
EDIT: added link to the image of languages along the axis of abstraction level
[1] https://kuomarc.wordpress.com/2012/03/05/the-uncommon-lisp-a...
[2] https://kuomarc.files.wordpress.com/2012/03/level-of-or-appr...
The huge amount of libraries for Python, C, C++, and Fortran make them the obvious choice.
Plus functional programming paradigms aren't always the best to do scientific programming. It can be done, but it's really not the right tool for the job, in most cases.