CS is unusually easy to learn on your own. You can mess around, build intuition, and check your progress—-all on your own and in your pyjamas. It’s easy to roll things back if you make a mistake, and hard to do lasting damage. There are tons of useful resources, often freely available. Thus, you can get to an intermediate level quickly and cheaply.
Wet-lab fields have none of that. Hands-on experience and mentorship is hard for beginners to get outside of school. There are a few introductory things online, but what’s the Andrew Ng MOOC for pchem?
This is more about ML than CS. ML is fundamentally about developing general-purpose algorithms applicable to a wide range of problems. If your job is using ML to solve problems in chemistry, it's more about chemistry than ML, and a chemistry background is more important than an ML background. It's unlikely that you have to develop novel ML methods for the problems you are facing.
I've seen the opposite in bioinformatics. While dedicated bioinformatics programs are now common, you still see many CS / mathematics / statistics / physics / EE people moving to bioinformatics after bachelor's / masters's / PhD / postdoc. In some bioinformatics jobs, you often have to solve new computational problems, and it's easier to teach enough biology to people with a methodological background than the other way around.
I've seen both in bioinformatics, because the field is so wide now.
1) Bioinformatics as tool-building, algorithm-dev: you're right, you don't need to know much biology there if the problem is defined well.
2) Bioinformatics as a tool to answer biological questions: here I've seen ML-background people really struggle, either developing stuff that's not useful or reinventing-the-wheel-but-now-it's-deep-learning. I've seen ML people present their fancy plant disease image detector which turned out to be pretty good at spotting 'yellow' - very good at training accuracy and benchmarks, does not add anything to what people in the field are doing.
In regards to 2), it sounds a bit directionless to be proposing stuff people don't need? Isn't that more of a problem of selecting relevant problems to solve, and getting supervision on your ideas?
Yeah that's the problem! The ML people and the biology or agriculture people don't talk, they're not in the same building. A biologist might see the ML-person's work only after it's published.
On the flip side, software development and engineering rigor is largely absent in academics, as has been discussed previously here on HN. This is enough of an issue to make replicating research even when the code and data are provided, but it's an even bigger issue trying to turn academic code into a product.
CS is unusually easy to learn on your own. You can mess around, build intuition, and check your progress—-all on your own and in your pyjamas. It’s easy to roll things back if you make a mistake, and hard to do lasting damage. There are tons of useful resources, often freely available. Thus, you can get to an intermediate level quickly and cheaply.
Wet-lab fields have none of that. Hands-on experience and mentorship is hard for beginners to get outside of school. There are a few introductory things online, but what’s the Andrew Ng MOOC for pchem?