I'm trying to get back into my chosen field, specifically in computational chemistry and drug design. It's rough sledding as they say, since I am an older candidate who is hardly up to speed on where things are now vs. circa 2005.
For the past 18 years - since I left academia - I've been bouncing around as a "data scientist," working in many fields but not what I really want to do and/or am actually good at doing.
In my experience it's almost always the case with industry that you end up in some adjacent niche that isn't quite exactly what you want to do, even if you never leave the field of your schooling. By that I mean it may be a lot easier to get into computational chemistry for something other than drug design.
It's a weird time now with software eating everything and many science jobs asking for PhD in <our science branch> or Computer Science (!) as requirements. So I'd assume there's some angle in which your experience provides an advantage.
@JamesianP - You're exactly right about the heady requirement lift. And there are plenty of widely-defined computational chemistry jobs, especially when you throw in the bio- prefix.
As for the adjacent niche bit, right again as you are about experience. My time as a so-called "data scientist" was never really the best fit, and I could never compete with the folks who do Kaggle competitions and have a real passion for AI/ML (which, to me, is one of the defining points of "data science" today).
However, I've logged a lot of time over the past 30+ years in front of the terminal, so fingers crossed!
@deskamess - I believe a Master's is the minimum buy-in, and Ph.D. is preferred. My Ph.D. area is quite narrowly in theoretical chemical physics, but was all heavy computation and focused on quantum and classical dynamics.
The areas of study are going to be computational chemistry, pharmaceutical chemistry, cheminformatics, or something closely related to those. Whether you work in a lab or in front of a computer depends on what you study and whose lab you study it in. You can't go wrong with a Ph.D. from UCSF, I can tell you that (I was a postdoc there), so browse the faculty pages.
Have a look at some of the open positions at, say, Roche/Genentech. There are some great open source resources and some industry-dominant ones with which you'll be expected to have some experience in (Schrodinger's software is one of them). Domain knowledge, of course. QSAR/ADME and similar acronyms. Docking, free energy perturbation, bioinformatics. Python/R/SQL.
AI/ML is of course hot in pharma but not necessarily useful for all situations - I happen to not be much of an AI/ML guy and won't be penalized for that, which I cannot say about almost every job I've had or applied for.
What got my foot in the door with drug design was postdoctoral work on molecular dynamics simulations of large systems that are physiologically relevant - motor proteins - and I was teamed-up with experimentalists who were studying the same systems.
More than happy to share more, my contact info is in my profile.
Thank you for the detailed response and your offer to share more. Let me start with the resources you have already shared so I have a decent background before I come back with questions.
Wish you success in your efforts to get back into the industry.
For the past 18 years - since I left academia - I've been bouncing around as a "data scientist," working in many fields but not what I really want to do and/or am actually good at doing.