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These all seem like things you can figure out during the hiring stage. You could be losing out on some really talented employees by simply distrusting all junior candidates due to some prior incidents.

I've worked with some absolutely top-notch candidates straight out of college, and some mediocre ones which plenty of experience.

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There is always some exception to the rule. Frankly the issue is more the number of topics one needs to learn to actually add value as a data-scientist. Junior data scientists need a hell lot of supervision compared to analysts or data-engineers. Most of what it takes to be a data-scientist is acquired on the job rather than something learned in class. And yes some of that could be checked during the interview, but that would also mean a very low pass rate and a lot of time spent hiring on these roles. I would rather hire these people straight out of college in role where they would have much more chance to excel, and would need less supervision. If they are good enough then give them more modeling tasks.

Junior anything needs a lot of supervision, it's probably the defining trait of a junior.

"Junior anything needs a lot of supervision, it's probably the defining trait of a junior." - yes, but I would argue that junior data-scientists requires much more supervision than the same person doing a junior analyst or engineer role.

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