The incentives in academia are different. The objective is to publish, code is important only insofar as it allows you to achieve that goal. This is not to say

The incentives in academia are different. The objective is to publish, code is important only insofar as it allows you to achieve that goal. This is not to say that academics can't code, but even if you are a professor who cares passionately about making high-quality software, you're fighting uphill, because that's not what you're being evaluated on. If you want to make the argument about "two kinds of people", I think it's more about A-type/B-type data scientists in the industry. I'm really mostly a developer and not a data scientist, but when I assist in DS tasks I wear a distinct B-type hat, and that informs my perspective. A-type people have different priorities and that's fine; my gripe is when you try to import A-type practices in a B-type scenario.