(i) wrongly configured character encodings (suppose you incorporated somebody else's library that does a "print" and the input data contains some invalid characters that wind up getting printed; that "print" could crash a model trainer script that runs for three days if error handling is set wrong and you couldn't change it when the script was running, at most you could make the script start another python with different command line arguments)
(ii) site-packages; all your data scientist has to do is
pip install --user
(iii) "python" built into Linux by default. People expected Python to "just work" but it doesn't "just work" when people start installing stuff with pip because you might be working on one thing that needs one package and another thing that needs another package and you could trash everything you're doing with python in the process of trying to fix it.
Unfortunately python has attracted a lot of sloppy programmers who think virtualenv is too much work and that it's totally normal for everything to be broken all the time. The average data scientist doesn't get excited when it crumbles and breaks, but you can't just call up some flakes to fix it. [1]