To the point about data scientists - there isn't really a way you could solely hire the software engineers, right? So there is value in Python allowing the data

To the point about data scientists - there isn't really a way you could solely hire the software engineers, right? So there is value in Python allowing the data scientists to iterate much quicker than they could if they were writing in a "production-ready" language. I agree there's work to be done in closing that gap so you don't need an extra software engineer for productionalizing mostly-working Python (and I'm excited about tools for managing large-scale Python - e.g., several of my coworkers are trying out MyPy, which I haven't personally felt too much of a need for but seems like it could help), but the gap exists precisely because you can write something working in Python quickly, and it's not so much of an investment that you'd feel bad throwing it away if it doesn't work. The company that started with Python and spent years on a full rewrite made enough money with Python to survive those years. If they had started in a more "production-ready" language they might not have shipped at all, and if they did they might not have shipped the right thing. And at least for me personally, as an infra person, the question I'm evaluated on at the end of the day (or year) is "Did the infrastructure work," not "Did you write production-quality software to support the infrastructure." Some of the most critical software I've written (across multiple companies) has been 50 lines of Python shared via a Slack snippet or a shared homedir and only retreated into version control months later. There are a lot of problems that genuinely require only 50 lines of complexity, and the ceremony of a language like Java makes it much harder to understand what's going on. For those problems that do require umnanageable Django monoliths, by all means, write it in something else.