What most firms mean by "data science" is basically just using tools for data cleaning, visualization, and using APIs like black boxes for a few different kinds

What most firms mean by "data science" is basically just using tools for data cleaning, visualization, and using APIs like black boxes for a few different kinds of models. You are often not even allowed to take a software design approach to these tasks, and often you're just writing scripts in a hurry to address "business intelligence" fire drills. For those positions, I am sure that smart full-stack developers could easily pick up the statistics for data cleaning and quickly gain a passable understanding of the models consumed from APIs in a black box way. In fact, full-stack devs may be happier in these jobs due to the visualization and database components. A much smaller subset of data science is actually focused on solving novel business problems and may centrally focus on deeper knowledge of a given technique, like MCMC methods, deep learning, real-time classifier systems, etc. For these, you do tend to need more significant experience with the specific machine learning tools being used (or enough general skill in statistics to pick them up quickly). Smart people of all stripes could still learn that stuff, but it's a lot harder to see them being able to convince a firm to hire them in that capacity. The second type of these jobs is really, really rare though.