There's no a single simple answer, except: don't decide about the implementation first, instead competently and without prejudice evaluate your actual possibilities.
Also don't decide the language like "Python" or "Ruby" first. If you expect that the calculations are going to take a month, then code them in Ruby and wait the month for the result, you can miss the fact that you could have had the results in one day by just using another language, most probably without sacrifying the readability of the code much. Only if the task is really CPU-bound, of course.
On another side, if you have a ready solution in Python, and you'd need a month to develop the solution for other language, the first thing you have to consider is how often you plan to repeat the calculations afterwards. Etc.
There is a lot to be said for confirming that you have the right logic and then looking into something like PyPy/Cython/etc. or porting to a lower-level language.
Which language do you suggest which matches this?
I don't know many people who use the language "numeric error handling characteristic" but I know the benefit of already having good written high-level primitives (or libraries).
The other area I've seen this a lot is caused by the way floating point math works not matching non-specialists’ understanding – unexpectedly-failing equality checks, precision loss making the results dependent on the order of operations, etc. That's a harder problem: switching to a Decimal type can avoid the problem at the expense of performance but otherwise it mostly comes down to diligent testing and, hopefully, some sort of linting tool for the language you're using.