I disagree, even though python is the language I do most of my development in. But it probably depends on the problems we're thinking of a data scientist solving.

If you're doing a lot of work with matrices, model fitting in production, then python seems fine. However, a lot of data scientists I see are more like scrappy data analysis / visualization types, who are churning out small dashboards. In that case R's tidy verse and shiny are just incredibly fast to develop with.