Even among devs with very close speciality, the difference in productivity is immense. Take a iOS team, make them dev a MacOS desktop app, and see their output

Even among devs with very close speciality, the difference in productivity is immense. Take a iOS team, make them dev a MacOS desktop app, and see their output plummet in their of productivity or quality. They will end up doing a good job, but it will take between 6 months and a year to catch up with a specialized team. A data scientist is not even a somebody trained as a programmer. Their strong suit is data analysis, and it turns out one of the tool to manipulate data today are programming languages so their do it. But I as a Python trainer, I train data analyst regularly, and they don't have a clue about language ecosystems, how the OS work, data formats or reliable software architecture. They mainly want to output their graph, pdf report or other media to serve their conclusion. They may want to create some reusable algo, or machine learning model, but that's the limit most of them hit. If one take their code and put it in prod (which I know happens, don't get me wrong), that's not the data scientist fault. They are doing their job, in which programming is just one of the many means to an end, and is not their specialty.