[LeonardoTolstoy]

> Lack of familiarity with common data structures and algorithms

This part I 100% agree with. I adapt a lot of scientific code as my day-to-day and most of the issues in them tend to be making things 100x slower than they need to be and then even implementing insane approximations to "fix" the speed issue instead of actually fixing it

>"Big ball of mud" design

Funny enough this was explicitly how my PI at my current job wants to implement software. In his opinion the biggest roadblock in scientific software is actually convincing scientists to use the software. And what scientists want is a big ball of mud which they can iterate on easily and basically requires no installation. In his opinion a giant Python file with a requirement.txt file and a Python version is all you need. I find the attitude interesting. For the record he is a software engineer turned scientist, not the other way around, but our mutual hatred for Conda makes me wonder if he is onto something ...

>I think the appearance of "I'm just getting shit done" is often a superficial one, because it doesn't factor in the real costs: other scientists and engineers can't use their solutions because they're not designed in a way that makes them work in any other setting than the narrow one they were solving for.

For the record my experience is the exact opposite. The crazy trash software probably written in Python that is produced by scientists are often the ones more easily iterated on and used by other scientists. The software scientists and researchers can't use are the over-engineered stuff written in a language they don't know (e.g. Scala or Rust) that requires them to install a hundred things before they are able to use it.