This is not remotely true. It's true that there are lots of data scientists who can string together sklearn or R models. However, very few people know how these models work, when to apply them _and why_ or what concerns need to be addressed on data, metrics and deployment. Even fewer know how to improve these models once they catch all of the low-hanging fruit.