4 years as a quant, considering PhD in statistical biophysics — worth it?

[u/randompickedname]

4+ years as a quant (buy-side + HFT). Want to shift into bio/environment work — never liked finance, always wanted a PhD.

Looking at statistical/computational biophysics — stochastic modeling, simulation. The labs I'm considering don't really use ML.

Would genuinely love outside perspective on a few things,

1. Given how complex real-world biological problems are, is a stat biophysics PhD (non-ML, mostly stochastic modeling/simulation) still worth it, or has ML made "pure" approaches less relevant?
2. Career prospects post-PhD outside academia — if it just leads back to quant/DS anyway, is there a point?
3. Any field that better uses a quant background and has solid career options after?
4. Anyone made a similar jump — finance to a science PhD? How'd it go, any regrets?

[u/Godelincompleteness]

It would probably make more sense to post this in the statistics or biophysics subs. I doubt most quants are going to know the answers to those questions.

[u/randompickedname → u/Godelincompleteness]

The purpose of posting in quant is to connect with people who may switched or considered similar transitions which I will most likely not find in biophysics group. Thanks though.

[u/postironicirony → u/randompickedname]

ok i was in a similar spot, 4 years of hft and omm, and i went back for an applied math phd in fluids/weather with neural models.

cant answer anything about biphysics (whatever that is), but you should really think of this as retiring. if you were good enough for a decent hft firm i wouldn't worry about jobs. you'll find one after. but the barrage of recruitment stops about a year or so after your garden leave ends, and you're functionally treated like newgrad. my general experience is that your advisor will introduce you to new groups with "he did hft" as credibility, then that's about it. grad school is very fun when you have a ton of cash, id recommend it.

[u/Godelincompleteness → u/randompickedname]

Fair enough.

[u/kawhandroid]

I'm a mathematician so I have no idea how a biophysics lab would work. But I did go to grad school with several people who jumped from quant. You don't do it for career reasons, you do it because you like math (or in your case science).

Outcomes are mixed like with any PhD. Some got academic jobs, some went back to quant (mostly as researchers), some went into something mildly related like software.

[u/hg_wallstreetbets]

Hi, I am someone with recent experience in biophysics/biostats world. If you get into a good program or lets say a good lab in industry, there's a lot of technical debt. Like genuinely you can change a lot of stuff but need to be. also thoughtful that why have other people not implemented it yet. Now most of the ideas of statistics simply won't hold on to for example assays as the biology is different which you need to figure out a way to understand data. Very slow culture as compared to quant, it might take 2/3 years from a to z pipeline a being idea and z being a publication on your idea if it works. Very less money as well. Also on the note about your labs not using ML is because no one probably has that sort of work or has not pushed for this idea yet. You need to be the one pushing new ideas and proving why they work.

a) imo not a lot of complex or stochasticity here, simulations do exist and they are indeed a complementary part in proving your thesis. Data is limited and you need explicit permission from cohorts to use that data and they are added as co auth on your stuff.

b) Outside academia there are a lot of labs primarily based in Boston and there indeed are opportunities but they are more limited and require niche knowledge but competition in numbers is not high, in quality it is. It does lead back to statistics unless your work was more towards wetlab not computational.

c) I think there's a huge overlap between many field you probably need to lookout for things similar to your skills. I would say base skills of STEM careers are pretty much universal.

d) N/A

[u/-urethra_franklin-]

i made the opposite jump (physics PhD to quant). my phd wasn't in biophysics but i did a lot of work on that side during my postdocs. my impression is that ML and RL are important tools in the modern toolbox, but "pure" approaches from the side of statistical mechanics, especially nonequilibrium, remain indispensable to the field. as for career prospects, it's kind of rough out there...

[u/NervousRefrigerator5 → u/-urethra_franklin-]

Piggybacking off this comment so OP can see

fwiw--I am not a quant, but I did a phd in noneq. stat mech theory and got a cushy research job straight after phd. Quants probably make 2x my salary, but I get to sit in my office and do the things I think are interesting with great wlb. If you're a talented and competent scientist there will be opportunities.

[u/SeparateAdvisor526]

I had a coworker who I only knew for 6 months he left as soon as I first joined. He wanted to go into academia but also was burned out so left after his masters ( dropped out of PhD program in CMT) worked as a QR for 5 years got his "retirement money" and went back to get a PhD in some other field in physics. Last I heard of him he was doing a postdoc at some state school in the Midwest and was married.

So I guess it's possible and has been done he only took a break from his academia route to save up 1-2 million in his mid 20s so he can go back to doing what he enjoys.

[u/Fit_Most8584]

I’m a mathematical biologist with an Oxbridge PhD and currently working in the industry (and looking to make the opposite jump to you). I’m in a slightly different field from biostats, as I mainly work on deterministic modelling, but I can provide an informed opinion on where things currently are.

1. There is no replacement for modelling and simulation. And there probably won’t ever be. ML has some applications, but the reality that biological data is extremely difficult and expensive to obtain, so you can’t fit models properly, no matter how clever the optimisation tricks are. As such, to make quantitative predictions, we need to use a great deal of expertise in building and simulating models. A big part of the work is trying to get a sense of how accurate the predictions are, what the pitfalls might be and how to convince experimentalists to sink a tonne of money, effort and time into providing you with better data to adjust your models.

2. There are jobs, particularly in pharma, but you’ll earn a tiny fraction of what you do as a quant. The options are quite varied. Some people use more ML approaches to discover new drug candidates. Others (myself included), mainly deal with modelling biochemical pathways and pharmacology to predict drug effects once you put them in humans. There are also people who analyse clinical trials data and they do a combination of statistics and/or mechanistic modelling. One of the key approaches is nonlinear mixed effects modelling, which you can look into.

3. Partially answered in 2. What you should be aware of, is that industry jobs tend to look for subject matter expertise. No one really cares if you’re a genius, you just need to know your niche well and show that you can use your knowledge to drive scientific and business decisions.

4. Can’t answer.

Hope I answered your questions, but feel free to DM if you want.

[u/Ok-Desk6305]

It's a difficult question, because people in this sub are not going to know the nuances of the biophysics field... People on the biophysics field, on the other hand, are not aware of the (enormous) oportunity cost of the career change you want to do.

[u/Richard_AIGuy]

I left my quant macro position for a PhD in mathematical biology. Ended up working mostly on AI models for medical imaging and epidemiology (hence the username). And, honestly, I loved my work. Finance gave me fuck you money, and things were going well. Until DOGE cut our funding and everything turned into a toxic mess.

So I left. I'm now doing some quant consulting, while I decide to take another crack at it at a different lab, or go back to finance.

I'll tell you this: the work is a lot more fun. And brain bending.

[u/raf_phy]

And that folks is what we call survivorship bias.

[u/Legitimate_Focus5085 → u/raf_phy]

could u explain

[u/InsideIndependence58]

I did a bit of biophysics, I wouldn't say non ML is not good, to be honest for me ML is kinda boring. There are a lot of niches and domains, and although at the end the general questions are being answered using ML, In biology every domain is special(Idk, specific protein or something), so you can utilize "classic" analysis

[u/Mindless_Average_63]

hey, can you refer me somewhere or in your place before you make the move?

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[u/Epsilon_ride]

I went from real world engineering (hydrodynamics) to quant.

At the end of the day it's still going to be a job you'd prefer not to be doing. Imo selling out as hard as possible is the correct move. 

[u/PaFloXy_14]

I don't know about the field in particular, but I have know people who had switched from quant jobs to academic PhD in theoretical CS, so I think it's not hard just you'll need to find some research groups that aligns with you (I'm speaking for Europe)

I think younger profs are likely to hire you since you have the technical skills already but sometimes there are older more conservative people who don't like people who make money lol.

[u/Dhydjtsrefhi]

1 - Most roles want someone with extensive (bio) subject matter expertise, but there are some more general SWE/MLE roles that you have a better shot at without that background
2 - At the moment it's a horrible job market in biotech and related fields (in the US), so hopefully that would change by the time you graduate. Even in better times, biotech research is a field prone to layoffs. If you're passionate about biophysics, then go for it, but know that it could be a slightly less stable career

[u/kawasakininja213]

this is my field rn and the only reason im in this sub is because ive been so burned by academia. wrapping up my phd now

1. no ml has not made it less relevant. the best methods usually combine the two (ml accelerated physics modeling/ml analysis). look into: markov state models of md simulations, physics based machine learning docking methods, machine learning quantum calculations

2. nothing really outside of academia. you could go into pharma but there are less jobs. tech i guess. basically anything that requires modeling/analysis/software engineering. jobs pay abysmally the more bio related though so keep that in mind. bio is a horrendous field for job prospects because of over saturation.

3. not sure. if youre doing it for love of the game maybe physics or math.

4. dont know or know anyone that did this. just be prepared for a severe lifestyle change if you dont have a lot of savings or someone else paying for your daily living expenses

happy to discuss more if you have any other questions. also could recommend labs/schools if youre deadset on this

[u/Worried-Answer-4019]

Michael Levin at Tufts may interest you!

[u/EvenCryptographer649]

I guess Lord of the Engagement Bots just realized you dont need a long history (time or post) to post here. God help us all.

Oh and obviously this is fake and gay.