Data Science to PM

Joined WSO yesterday, still figuring out all these jargons and seemingly random abbreviations I keep seeing across posts. I am currently pursuing an MS in Computational Data Science at CMU.

I was wondering what were the odds of transitioning from a data scientist at a place like point72/ Millennium/ DE Shaw/ AQR to a PM. Is there way wherein one can work for 3-4 years as Data scientist and get 2-3 levels of CFA on the side and then make the transition, or is this unheard of. Let me know if there is a fund where this sort of a trajectory has been seen in the past.

I will be interning as a data scientist (L/S Equity Research Team) this summer at one such place and hope to receive a full time offer as a Data Scientist (I graduate in December 2024). I have 2Y SWE exp at a large financial services company in my home country.

Looking forward to your insights.


Transitioning from a data scientist role to a portfolio manager (PM) in the high-stakes world of hedge funds like Point72, Millennium, DE Shaw, or AQR is indeed a challenging but not impossible journey. Based on insights from the Wall Street Oasis community, here are some key points to consider for someone in your position:

  1. Leverage Your Data Science Expertise: Your background in computational data science and experience as a data scientist in L/S Equity Research can be a strong foundation. Hedge funds value deep analytical skills and the ability to derive actionable insights from complex datasets. Your technical expertise can give you an edge in quantitative analysis, a critical skill for a PM.

  2. CFA Certification: Earning CFA levels while working can significantly bolster your finance knowledge and demonstrate your commitment to the field. It's a recognized credential that covers a wide range of investment topics. While not a guaranteed ticket to a PM role, it certainly enhances your profile.

  3. Internal Lateral Moves: Within hedge funds, there are precedents of professionals transitioning from quantitative and research roles to PM positions. This path often involves proving your investment acumen and building a track record within the firm. Networking internally and expressing your long-term career aspirations to mentors and managers can open up opportunities.

  4. Building a Track Record: As you mentioned, not having a direct PnL responsibility can be a hurdle. However, contributing to successful strategies and demonstrating your impact on investment decisions can help build your case. In some instances, professionals transition to roles with more direct market exposure (e.g., trading or quant research) before moving into PM positions.

  5. Educational Pathways: While working, consider engaging in further finance or investment management education. This could be through formal degrees, executive education, or specialized courses in portfolio management and investment strategies.

  6. Networking and Mentorship: Connect with professionals who have made similar transitions within your firm or the broader industry. Their guidance can be invaluable in navigating your career path and understanding what skills and experiences are most critical for becoming a PM.

  7. Patience and Persistence: Such transitions take time and require building a solid foundation in both your current role and in finance. Be prepared for a long-term effort, continuous learning, and seizing opportunities as they arise.

In summary, while transitioning from a data scientist to a PM is challenging, it's not unheard of. Your strategy of gaining experience as a data scientist while pursuing CFA certifications is a solid approach. Focus on leveraging your unique skills, seek out mentorship, and be proactive about your career development.

Sources: Transitioning from tech to a serious finance job (yes, you read that right),, Q&A: Managing Director at Large Global Asset Manager, Moving from Quant researcher to Quant PM, Q&A: Alternative Path to F100 Corporate Strategy w/ Exit Details

I'm an AI bot trained on the most helpful WSO content across 17+ years.

You can't transition from data science to PM in discretionary


You think you’re gonna waltz into a premier HF and somehow, in 3-4 years, will become a PM? You would have to both godly and lucky. Be real man, you have a better chance being an MD at 27 than a PM. Nobody gives a flying fuck about the CFA once you’re in the buy-side game: you cannot hide your objective performance behind a 3-letter certificate.

I am all for dreams and optimism, but this is just delusional. You also go to a non-target, and these people usually don’t hire college kids, much less so from a non-target.

But hey, it is possible to become a PM. Just not something realistic though. Think of it as being dropped into an F1 car and being told to finish in a podium position…despite never having driven any car but in a videogame and hot wheels.


My education is 100% stem and thought a CFA could also be useful to gain theoretical insights into finance in addition to the on the job learnings . Not looking at a way to prove mettle with a CFA. How can a DS demonstrate skills which could convince a fund to give him/her control of capital. Is there any history of something of this sort happening? And if yes, how long does it take? If no, what do Data Scientists in HFs do after 7-10 years in the industry as DS. I’m sure they would want more of a say and a bigger cut of the pie at this stage


Nobody gives a shit about a data scientist simply because it is all about Portfolio Management. It is already unbelievably hard to come up with winning plays, let alone managing a portfolio. In order to generate P/L you need to fulfill 3 things: 1) you pick correctly. 2) you time it correctly. 3) you allocate correctly. Do you know how hard it is do these 3?? And besides that, you need to figure out Sharpe/Traynor/Jensen, correlation, volatility…Portfolio Management is a stressful, highly skilled endeavor. It is much easier to be a data scientist man, come on.


I look at the PMs at p72 and MLP on LinkedIn a lot of them( over 50%) seem to be have pretty unspectacular backgrounds( college and prev employment).In contrast, the STEM positions seem to have a higher barrier of entry. Only T-10 CS schools from what I gather. The data scientists usually transition to quant research after a few years, but how much control they have ,Idk. ER is probably the wrong place to ask this, will try to reframe this in HF section.


Let me put it to you this way. If you are at P72, you could be a cadaver from Mars for all anyone cares. P72 is sufficient reason alone to consider someone as potentially great. It ultimately all depends on P/L, and they probably got there (and stay there) cause they make money


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