I've seen data scientists who take the "no assignments" idea as an excuse to focus on "interesting" vs "practical" data problems (and IME, like yours, the "no a

I've seen data scientists who take the "no assignments" idea as an excuse to focus on "interesting" vs "practical" data problems (and IME, like yours, the "no assignments" mindset arises mainly in academia, not industry - I never even finished a MS, but even there saw far fewer academic 'assignments' than industry ones). And if that project is high-budget or has ample data, the stakes get a lot higher. I've seen teams lose their projects and leaders lose their position because the results weren't there. It doesn't matter if they did the math right or wrong, if the results weren't there due to randomness or outside of their control, etc.... lots of people don't want to spend hundreds of thousands to millions a year on a team that hasn't produced working products. The worst were cases where the project had an impact, but a negative one (because of an over-focus on specific areas, say, without getting sufficient full-business context). And often these were cases which some more requirements gathering up front could've avoided, but "you can do all the science and math right and still fail and lose your job" is a particular pitfall that I don't think most academic transfers really appreciate at first.