There is a market, and it's called libraries. Eventually you will use a language where software carpentry and code reuse is a core feature, and tested, modular libraries for not only core algorithms, but also deployment and dev-ops stuff (like managing a compute cluster on the cloud) will have standard approaches.
This is starting to shape up on the Python side of things, but it has stagnated a little bit. People who can and do write the foundational code are oftentimes too focused on making the code work, and not at all focused on improving the quality of the ecosystem that their code is part of. Open Source is a great mechanism for many things, but polishing up the last 20% is not one of them.
"Eventually you will use a language where software carpentry and code reuse is a core feature."
Well, to some extent products like MATLAB solve this problem. For better or worse, I trust Matlab's ability to generate a (pseudo) random number, parallel process my functions, invert matrices, etc., etc.
On a broader level, thanks to the specialization of academia, chances are that the code I want to write isn't duplicated by others. Even if it is, I still have to trust them to have written it well - which is the whole problem here.
This is starting to shape up on the Python side of things, but it has stagnated a little bit. People who can and do write the foundational code are oftentimes too focused on making the code work, and not at all focused on improving the quality of the ecosystem that their code is part of. Open Source is a great mechanism for many things, but polishing up the last 20% is not one of them.