Sometimes I think a company (not having the DS experience themselves) mistakenly over-hire DS roles in today's hype of "AI" when their data is mostly run-of-the-mill and only requires simple linear models that can be architected an understood by a stats/math-savvy engineer. Even then, a good DS is still useful (even linear models can be complex: e.g. what priors do you want to use? Do you want a multi-task solution? etc.), but maybe not worth the cost.
Reality is that data science is here to stay. It's coming out of the honeymoon period, and things may never be as hyped up as it has been the last decade, but that's probably a good thing for the field. Everyone will probably move on to hating the next up and coming thing. I have a hunch it could be something in data engineering because, while not exactly new, it is absolutely the next "data science" in terms of demand, and with products like Snowflake having so much hype behind them, it seems the backlash will be inevitable.
I remember an era before "data scientist" was a job title. When we (programmers) would analyze data to see if we had enough information available to identify the problem, if not, fix that, then come up with a strategy to solve it, test, and finally deploy the model. The fun part was trying different solutions and analyzing the data. It also felt awesome to deploy a product that worked like "magic." Product owners didn't know or care what a neural net was, they were just happy it worked.
Now there are tons of data scientist out there who take the easy, fun, rewarding work and try to skip over the nitty gritty implementation details. Then management thinks engineering is incapable of doing such work, and the only time we get the opportunity to do something fun is to do so behind the scenes.
There's a huge variety in DS responsibility and background between companies.
Maybe I'll come back in a few hours, but for now I'll stay away.