In all honesty, after 6 years of studying, with 4 of those years studying ML, to be told that I lack experience with some particular stack as the ~~excuse~~ reason for rejection feels like a slap in the face.
And all that ignoring everything that expects 3+ years experience for entry level positions.
There is this interpretation of Bayesian networks that the parents are the true causes of a node and to predict what happens if you change a variable you need to remove the edges from the parents to that variable from the network. And then I study what you can do with that method
Tl;dr I moved to Japan and worked in ML (ish) job. Once you start working it becomes remarkably easier to not be scoffed for lack of experience (even if you learned very little in that job)
Job security is high in Scandinavian countries and as an consequence people hire very risk-averse. risky/not so established jobs such as ML positions, they'll be actively looking for reasons NOT to hire you
ML is the sexy wing of the tech industry, so it tends to attract the people who are willing to put in the hours (for interviews as well as towards work).
It enables the business to say they invest in R&D (tax writeoffs, marketing) and it also makes it easier to hire the grunts who'll do the scut work, thinking they'll one day be working on something more interesting (which they won't be).
Competition for bullshit "data science" jobs isn't that tight. For genuine research that actually matters, it is, and that's because most of these positions exist as recruiting tools (you're getting paid to let the company say you work there) and that's always going to be a thin ledge to try to perch on.
Today, the competition for research positions in a "sexy" STEM field (think quantum computing, black holes, DL, RL) is quite high. DL just happens to be a field where it is possible to get into research without investing too much time while also making a lot of money, so this is a dream job for many.