I have only a Bachelors (math) and work at a non profit research institute as a software engineer. I've contributed to a number of publications and first authored one, and had my name on some grant applications so I guess I count as an academic.
I think the bio field is desperate for people with software skills and it is actually pretty easy to get into if your willing to take a pay cut vs industry software rates.
On the other hand, I was getting recruited by a machine learning company based on my open source contributions etc but interest dropped off when they found out I didn't have a PHD (and wouldn't move to the bay area).
I think the reality of bio research is that it does involve a ton of mundane work and that even when working on a novel project we spend a lot of time doing server admin, cleaning, processing, reformatting data, building databases and data portals etc. Every time the scale of a study increases one of our tools breaks and getting things to scale often involves more hacking then pure research.
Occasionally one of us engineers come up with a novel approach worth publishing and more often we make a contribution to a larger study that is novel biology even if it isn't novel analytically.
I would contrast this with, say, machine learning researchers chasing percentage points on well established data sets like MINST or even doing kaggle competitions. Our data tends to be less settled more noisy, heterogenous, missing etc and have questions that are less well posed (eg often one disease is actually many with similar symptoms) so there is a lot more mundane wading through things.
I'm not sure what my prospects are if/when I decide to change jobs but I'm hoping that my open source contributions and papers would convince an employer that I know what I'm doing enough to continue working on novel problems.
You have to realize that most post-doc's, so people who are in their 30's-40's and have the years of tenure and education as a doctor with residency make like $45-50K; that is, if they're lucky to get it.
So for a code monkey to come in and tap away on his stupid keyboard to make like $75K is a travesty as far as the research academia is concerned.
But in research, you're better than the sell-out's trying to make people click on ads and shit though. So part of your paycheck every week is padded with tokens of self-righteousness and moral superiority that you can redeem in times of needed philosophical consolations.
It is both. And the people at the top are various grant funding agencies that often move at the pace of government and are used to the post doc pay scale. Things are changing but the salaries places like google and amazon pay are just hard to match on a government grant.
Academia, because it is fueled by grants, cannot necessarily adjust pay to compete with the demand from other sectors, no matter how much they might like to.
Beyond that, good programmers are stupidly helpful, but not mission-critical.
I think the bio field is desperate for people with software skills and it is actually pretty easy to get into if your willing to take a pay cut vs industry software rates.
On the other hand, I was getting recruited by a machine learning company based on my open source contributions etc but interest dropped off when they found out I didn't have a PHD (and wouldn't move to the bay area).
I think the reality of bio research is that it does involve a ton of mundane work and that even when working on a novel project we spend a lot of time doing server admin, cleaning, processing, reformatting data, building databases and data portals etc. Every time the scale of a study increases one of our tools breaks and getting things to scale often involves more hacking then pure research.
Occasionally one of us engineers come up with a novel approach worth publishing and more often we make a contribution to a larger study that is novel biology even if it isn't novel analytically.
I would contrast this with, say, machine learning researchers chasing percentage points on well established data sets like MINST or even doing kaggle competitions. Our data tends to be less settled more noisy, heterogenous, missing etc and have questions that are less well posed (eg often one disease is actually many with similar symptoms) so there is a lot more mundane wading through things.
I'm not sure what my prospects are if/when I decide to change jobs but I'm hoping that my open source contributions and papers would convince an employer that I know what I'm doing enough to continue working on novel problems.