Apart from the odd library I have rarely found much benefit to using both languages for DS as you end up expressing the same paradigms just in different syntax. And I think for good reason too - the basis of the tools used to do data science arent in the languages themselves but the packages built for the task which is why there's often an R equivalent of a Python package and vice versa. So in effect almost no one 'uses' R/Python for DS as much as Rube-Goldberg highly-optimised compiled libraries together using different syntax.ie dplyr/pandas/scipy/ggplot etc are the real stars of the show.
Rather than R vs Python I hope one of two things happen. Either both languages get replaced by a 'better' ML language eg Swift / Julia giving us users a 'turtles all the way down' experience and removing the reliance on complied packages. Or, second option, they get relegated even further into being nothing but glue between some common data formats specific to the type of work found in DS allowing you the user basically a choice between syntactic-sugar of one glue-language versus the other. Something like Apache Arrow springs to mind but I'm not sure where they are at the moment