4 ms·
I haven't left software behind but did pivot toward working in a biomedical lab and investing a lot of my time and effort in learning biology and prioritizing s
by iskander 11y ago
I haven't left software behind but did pivot toward working in a biomedical lab and investing a lot of my time and effort in learning biology and prioritizing science over engineering.
The reason for moving away from software was that it's vanishingly rare to do important work without expertise away from your laptop. As much as startups like to say they're "changing the world", I felt that most meaningful changes are simply leagues beyond the kind of complexity you can solve with coding.
- mkaziz 11y agoHow did that end up working out for you? I've been thinking about such a pivot, but am concerned that my lack of domain knowledge would make it not worthwhile to pursue. Do you have any pros and cons to share?
- iskander 11y agoIt's great, I'm really happy to be learning so much new and different material and equally happy I didn't go to Silicon Valley. I think I had previously carved a little niche for myself where I felt like an expert and didn't even realize the scope of broader (more important) problems about which I knew very little. Pro: * Learning about genomics, immunology & cancer * Working on clinical trials (i.e. might help a real person) * Seeing how science works as an institution (interesting peek inside the sausage factory) Con: * Doctors (& some biologists) tend to be more hierarchical than I'm used to from CS. You sometimes need to flash credentials to be treated respectfully (I have a PhD in CS, so that helps). * Hospitals and their bureaucracies are insane. So, if you want to actually have patient impact you'll have to deal with some really byzantine processes (and the madmen who create them). * Bioinformatics tools are crappy and their authors have few incentives to learn software carpentry. Related: file formats are designed to solve a specific problem and then get sloppily extended by everyone else. * Biology accumulates a lot of historical baggage in its naming conventions (i.e. weird names are often explained by "SoAndSo thought it interacted with SomeOtherProtein in 1953"). This makes learning a lot harder than it could be.