I work in AV development, and the number of refugees from the hard sciences is astonishing. I guess I don't blame them, but it's weird to have a physics PhD report to you and be able to use only a fraction of the skills she spent a decade and hundreds of thousands of dollars gaining.
This isn't quite a complaint: adopting math and physics PhDs (first as a mentor and now as a TLM) that are seemingly unproductive has been a consistent secret weapon of mine: they're often written off as unproductive, but it's not difficult to turn someone that smart into a solid engineer with a few months of guidance, as long as they're willing to learn. In specific domains, a subset of their actual skills are a good fit (they tend to be great at math), but it just signifies an unfortunate and fairly significant misallocation of resources.
Physics PhD student here. Yeah, we're all playing the lottery for the dream of a life studying the universe.
It's the exact analogue of how serious student athletes throw away their whole undergrad education for a shot at a pro career, usually to end up injured and useless a couple years later.
I don't know about that. If you fall short of your aspirations at the PhD or postgrad level, there's always quantitative finance or any number of other lucrative careers where your background will at least get you in the door for an interview.
Except, for so many of us who have studied hard science, the idea of a career in quantitative finance is the exact analog of the purebred racehorse who breaks his leg on the track and gets taken out back for an acute brain-scrambling.
As the example originating this subthread shows, quantitative finance isn't even close to the only option for a lucrative career available to those educated in the hard sciences.
Most STEM PhDs (and especially in the hard sciences) are funded from mostly government sources. It’s seen as the country making an investment in top talent.
Nobody (who is any good) in the USA pays money for a STEM PhD. You get paid to do these degrees, albeit not a lot but a lot better than paying yourself!
You do get paid but quite little. I still took on debt during my physics PhD for lifestyle reasons (my significant other was 2k miles away, so flights were prioritized over savings).
I went to graduate school early 2000s. 6 years, average stipend < $15k and still had to pay student fees. Then a couple post docs for 5 more years at $40k.
There was significant opportunity cost, but now I've had 3 jobs that were all reasonably well paid that I only got because of the fanciness of my degree. I'm _not_ saying that's the right sorting for hiring managers to do, but it is what happened to me.
Net-net, I don't know if I'm even, behind, or ahead financially. I do know that graduate school was one of the best experiences of my life, my deepest friendships were formed in those years, and I miss pursuing my passion at that level constantly. So, I guess I'm in the position of not really caring if it lost me money.
Right I know, but I was making the assumption that they had a related undergrad, and more importantly, accounting for opportunity cost. Five years of working time for someone smart and quantitative enough to do a physics PhD is easily over six figures.
Sorry, autonomous vehicles. We use the term so much internally and in industry-adjacent conversations that I have a blind spot around the fact that it's not an obvious acronym at all.
I didn't mention a loan, and was thinking primarily of opportunity cost (and to a lesser extent, undergrad, though the skills gained in undergrad tend to be the ones directly useful for their current work).
I tend to treat opportunity cost like inflation adjustment: in most cases, failing to include it is an error, so I do it by default.
This isn't quite a complaint: adopting math and physics PhDs (first as a mentor and now as a TLM) that are seemingly unproductive has been a consistent secret weapon of mine: they're often written off as unproductive, but it's not difficult to turn someone that smart into a solid engineer with a few months of guidance, as long as they're willing to learn. In specific domains, a subset of their actual skills are a good fit (they tend to be great at math), but it just signifies an unfortunate and fairly significant misallocation of resources.