To hit a target, first you have to see it clearly. The term "Data Science" covers a broad collection of jobs, from statistician to machine learning/pattern recognition/AI expert to DBA to business analyst to visualization/animation expert to cloud/cluster/Hadoop expert to general data wrangler.
The skills required for each DS role vary a lot. I wouldn't expect a cloud expert to have learned about the Hadoop stack or HPC workflows in school, at least not to a useful degree. The same goes for DBA or business analyst or data wrangler.
But statistics and ML lie at the other end of the spectrum. These roles require a hierarchy of formal skills that are rarely mastered outside of college. They're expected to keep up with the research literature or formal techniques, which almost always requires the math skills of an engineer or mathematician.
Remember, HR everywhere is technically clueless. If management doesn't tell them the precise set of skills needed for the job, they'll minimize risk and ask for more expertise and experience than is needed -- usually in the form of excess degrees or prestige or buzzwords. The best cure for this is to bypass HR and go straight to a technical manager who knows what s/he wants. That's hardest at large corporations, who tend to outsource their HR needs to the lowest bidder.
At a smaller company, a lack of degree will matter less. If you can convince them you know what they need RIGHT NOW and can learn future material quickly, that's what they want to hear. (That's probably what the bosses of the startup did).
Or if you're targeting a specific project, then if you can show (e.g. via Kaggle or an online portfolio) that you clearly have the needed skills and you're not just a script kiddie, that speaks a lot louder than a mere degree (especially if it's over a decade old).