It is EVERYWHERE. I recently had to interview a bunch of data scientists, and only one of them knew SQL. Surely, all of then worked with python. I bet none of them even heard of R.
The focus of SAS and R were primarily limited to data science-related fields; however, Python is a far more generic programming language, thus the number of folks exposed to it is wider and thus the hiring pool of those who come in exposed to Python is FAR LARGER than SAS/R ever were, even when SAS was actively taught/utilized in undergraduate/graduate programs.
As a hiring leader in the Data Science and Engineering space, I have extensive experience with all of these + SQL, among others. Hiring has become much easier to go cross-field/post-secondary experience and find capable folks who can hit the ground running.
Often they'd be doing very simplistic querying and then manipulating via DATAstep prior to running whatever modeling and/or reporting PROCs later, rather than pushing it upstream into a far faster native database SQL pull via pass-through.
Back in 2008/2009, I saved 30h+ runtime on a regular report by refactoring everything in SQL via pass-through as opposed to the data scientists' original code that simply pulled the data down from the external source and manipulated it in DATAstep. Moving from 30h to 3m (Oracle backend) freed up an entire FTE to do more than babysit a long-running job 3x a week to multiple times per day.