I think this is great advice but there’s an important caveat which is career goals in AI / ML.
A lot of companies that say “data scientist” when they really mean “spreadsheet analyst” are places to avoid if you have career aspirations in ML. In the worst cases it can be a bait and switch (very common) to get overqualified people to babysit rudimentary analytics. Especially avoid places that might do this to pad their staff for any type of acqui-hire or investor signalling reasons, because your career goals will not be acknowledged.
In the best cases, it can be some befuddled IT manager who vaguely thinks they need “AI” but really they don’t have projects that would actually benefit from it. They might be sympathetic to your dissatisfaction in the reality of the job, but will have little power to do anything about it.
Somewhere inbetween is another very frustrating case: situations where the business or product clearly can materially benefit from “real” machine learning, and from the perspective of making customers happy & making money it’s a no brainer to invest time to research implementations, but risk averse management, often with no ability to gain an understanding of the benefits of investing in machine learning, or who want to act as credit / politics gate-keepers for an existing system, puts the brakes on it and retasks you on things that just waste your talent.