No, I don't think you are too late. The predictions that data science will be fully automated in the near future are, in my professional opinion, unrealistic.
I see the future of data science benefiting from better software design, e.g. transformative frameworks like pytorch and sklearn, which are powerful tools but hardly fully automated. We'll continue to need skilled workers who are current in the latest software stacks.
It also benefits from what I'll call the "lotto effect", where data scientists will occasionally multiply the bottom line by 10x or more. This is of course rare, but companies will continue to chase that fantasy and hire data scientists because it's too tempting to ignore.
My only advice would be lean heavily into the software side of things. There's too many data scientists who are novice programmers.
I see the future of data science benefiting from better software design, e.g. transformative frameworks like pytorch and sklearn, which are powerful tools but hardly fully automated. We'll continue to need skilled workers who are current in the latest software stacks.
It also benefits from what I'll call the "lotto effect", where data scientists will occasionally multiply the bottom line by 10x or more. This is of course rare, but companies will continue to chase that fantasy and hire data scientists because it's too tempting to ignore.
My only advice would be lean heavily into the software side of things. There's too many data scientists who are novice programmers.