quant vs. data scientist in fundamental investments


New Member
I've noticed there has been an upsurge in demand for data scientists from both traditional quant firms and also fundamental managers. I want to ask about,

1/ how easy is it to transfer between the two? I'm interested in certain sector focused L/S equity groups but at the same time I don't want to close off the possibility of working as a quant later down the track.

2/ whats the difference in work between a quant vs data scientist at large quant firms like twosigma/deshaw/citadel etc

3/ traditionally, quants and fundamental analysts are considered front office and have the opportunity to transition to a portfolio manager role and manage money. Will this also be open to a data scientist on a L/S team?


Floss, as I have been preparing for my job search and have seen 'data scientist' almost as often as 'quant' in the job title openings. What I have inferred from the roles' specifications and qualifications is that the data scientists are working on acquisition and scrubbing of the data, while the quants are running analyses on that data (gross over simplification). Most data scientist roles require C++/python experience and usually ask for computer science degrees MS/PhD. The quant roles are super diverse in their specs and quals, spanning from exactly what I listed for data scientist to more specialized familiarity with pricing models that you get in MFE programs. I think the data scientist label is just more specific, but both over lap quite a bit.

From what I learned working in a corporate environment(not finance), your work speaks for you and opens/closes doors. If you have the talent and drive, I don't think you'll get pigeon holed into a dead end career path. But that's just my un-informed optimistic self speaking. I'd too like to hear what someone with some real experience has to say.


Active Member
I'm dealing with this now. I have interest from a reputable fundamental firm who is looking for a "data insights analyst"--the job description involves 1) developing machine learning and NLP models on alt data to augment fundamental analysts' research and 2) acquiring, presenting, and visualizing data. My interest will be higher if I get the chance to focus on 1).