This stance always rubbed me the wrong way a bit. Effectively, code is one of the tools a researcher uses to do their work. As soon as their work interacts with other people, for example when publishing a purportedly reproducible study or supplying novel algorithms to developers, they have a responsibility to deliver proper work that can be used and understood by other people. This is something we expect of every other profession, yet scientists appear to somehow have no concern for such lowly ambitions.

To be clear, I’m not advocating for data scientists to write production-grade webapps. But I absolutely think they should be bothered to write code that fulfills minimal requirements, is reproducible, documented, and mostly bug-free.

I think data scientists tend to have a lot of overlap with computer people so expectations for them may be a bit higher, my experience comes mainly from physicists.

Reproducible, documented and bug free is fine, they care plenty about those things too, the issue is the "no you're doing it the wrong way, use this entirely different technology instead" being based almost entirely on ideological reasons.

If we take C multithreading as an example, with my superivising scientist, multithreading is fine, he's willing to put some time into learning how it works because it's valuable and has had a stable interface backed by a reliable body for a while now. But if tomorrow you came up to him and insisted that doing multithreading was wrong without a solid technical reason (eg actual bugs and an explanation of how the only way to fix it is to dump the existing code and spend a few months redesigning) you'd get shot down.