In social science research, "getting the model right" is often the goal (i.e explaining why something happens), and "making good predictions" is of secondary importance (i.e. good predictions only matter insofar as they indicate that your model is good). That's one reason you end up doing involved probability modeling, compared to just slapping it with machine learning like you'd do in an industry setting. The flipside is that, as you said, many social scientists don't have the programming chops to do it themselves.

The other big advantage IMO is that it straddles the space between "traditional stats package" (SAS, Stata, SPSS, Minitab, etc.) and "programming language". We here know that it's more or less the latter, but the "batteries included" standard library with first-class support for a "data-frame" type makes it feel much like the former, especially if that's what you're already familiar with. It's a gradual transition from a stats package to general-purpose programming, and you don't have to jump through hoops, install third-party packages for basic stats work, or deal with ugly/counterintuitive syntax.