There's a modest living to be made at the interface between "scientific programming" and "commercial implementation of programs scientists write". This is an un

There's a modest living to be made at the interface between "scientific programming" and "commercial implementation of programs scientists write". This is an under-appreciated niche because it takes a lot of work to get into it: you need to be a good developer and a good scientist with diverse experience. My way in was through experimental and computational physics, but there are certainly other avenues these days. Modern statistics is the biggest piece of the picture that every interesting area has in common. If you're interested in scientific programming you need to understand Bayes as well as algorithms etc. I have friends in psychology, biology, etc and we can communicate surprisingly well because we all speak the same statistical language. But more importantly you need to understand how scientists think. They are amazingly hard to pin down to the kind of specs developers need. For example, a guy on my team once said after talking to one of the scientists we were working with for a couple of days, "I now have a much better grasp of the problem, but I still don't know what the default value of this parameter should be." I spent ten minutes talking to the scientist and came back and told the developer "5", because I could tell from the way the scientist was talking that he had no clue if the number should be 3 or 10, but seemed to be favouring the lower values. I didn't need to understand the problem domain in detail to make that judgement, but to have a reasonable grasp of the psychology of working scientists. So far as I know, there's no way to get that without working as a scientist yourself.