I'm a data science hiring manager. Other things besides just the raw academic credential (a PhD which wouldn't typically be in "Data Science" anyway, i.e., even the PhD's are just Data-Science-adjacent) are publications, conference appearances/posters, generated data products/pipelines, and contributions to relevant software. (For me, in that approximate order.)

I'd work on building some of those, because you do need to stand out against the field @MonteCarloHall described. (Software engineer + undergrad math/stats)

Those kind of achievements would satisfy screening filters. Then of course you'd have to have knowledge to back that up. I think it's reasonable to say that typically this will be domain-specific, e.g. you would end up with a different background knowledge base for NLP than for spatiotemporal problems than for network/graph problem domains. With all the growth has come specialization.