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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.

[1]: https://github.com/libmir/mir-algorithm [2]: https://mratsim.github.io/Arraymancer/

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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. :)

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