But for the scientists that wrote the original code, maybe not. Maybe they think of this sort of thing as drudge work, something that doesn't really hold their interest. Their fun is in designing the mathematical concept, and turning it into code is just a chore.
So yeah, we could teach scientists. But even better would be if we could provide scientists with tools that are just naturally fast when expressing problems on their own terms.
It's not about fun. Sometimes it can be the difference between something being even possible or not. In this case, the author said they ran this algorithm hundreds of times. So changing it from 30 mins to 0.1 second makes things that were impossible before, possible. I don't find it fun at all to optimise, but I can see where I may need to in order to make things better, or possible at all... what I am suggesting is that anyone writing code, scientist or not, need to be aware of this - and know when they just MUST optimise.
Perhaps, for the "scientist" writing that initial code, their algorithm was also already the best that could be done because they just don't know much about algorithms and techniques to make them faster?! But once you learn about things like divide-and-conquer, work avoidance etc. it becomes pretty much self-evident when your algorithm sucks... it's really not that hard - definitely not for people whose job is stuff like climate science who have a very good understanding of maths.
Maybe by teaching scientists the basic "smart" algorithms and just a little about algorithm analysis (Big O notation), massive computation improvements could be had.