I think the issue here is that "data science" encompasses two very distinct branches of work. One answers to business needs and the other produces data based solutions for the product itself i.e you might have a data scientist who A/B tests your website design so you minimize your churn rate and the other is the team at uber eats who maintains the recommendation engine. While the distinction might not always be as sharp, the former makes up the bulk of data scientists in the market (and I suspect the OP is in that boat) with comparably simple interviews while the rest is the 5 step interview process with hackerrank test you are more familiar with.
I think the distinction is not so much on the domain/application. Rather it’s just that many Organisations decided to jump on the data-science wagon and don’t quite know yet for what qualities to look out for during hiring. And in second order as long as the predictive model is not included in a business process the over fitting is not as easily visible to the layperson stakeholders (and junior data scientists).
