If I were doing this today, I would prototype the problem in Python and after realising the startup penalty, would rewrite it in D's Mir [1] or Nim's ArrayMancer [2].
Life on a lambda is too short to pay 6-8 second startup penalty over and over millions of time.
Our problem is that we have a team of data scientists who are familiar with Python, plus a decent set of custom tools written in it, so changing languages isn't an option
that's often the current explanation for continued use of Pyhton and R.
Often it is a sign that the problem is not "big" enough (eg: not crunching truly large data sets) OR data science team gets disproportionate amount of goodwill (thus money) to spend on its foibles. :)