"data science" and "science" are quite different things. Science is the systematic and collaborative pursuit of knowledge in a long-term endeavour. It is based on sharing and open exchange of methods and tools. Numericas, mathematics and computational codes are just important tools to do that. As Hinsen in the blog post I cited above points out, the most part of important computational codes is written in one-off research projects which go for a few years, and the people who develop these codes normally have to move on and work for a different institution, if they manage to keep working in science. On the other hand, important codes and algorithms may be used for many many years.

"Data science" is a broad term but usually just means the application of numeric, and sometimes scientific, tools to commercial means. It is almost always done in companies. Typically, between such companies there is no open exchange of tools and methods, no exchange of knowledge, and no long-term use of generated codes. This is the reason why data science companies don't have the problems which Hinsen pointed out. But, they could become affected by a degrading suitability of Python for computational science, because their tools were initially developed by scientists.