HF Dta Science Career Trajectory
Interviewing for a data science position at a MM following ~2 years in alt data, wondering what career trajectories look like -- do comp and responsibilities change meaningfully over time? Where would I max out? How does bonus structure compare to analyst positions? Is garden leave similar or less common?
on a pod or in a central alt data team?
Pod or MM.
Sorry, meant at a MM: a data scientist working directly for a PM on his/her pod, or, in the MM's central data team (they all have them)
The former has better opportunities associated with it, you get to actually see trade ideas get put through, learn the ropes from a PM, etc., although also comes with downsides. Usually you are the only technical person on the team which means your presumably excellent coding skillset is never appreciated. You might become a second class citizen on the team; "plz automate this thing" (which they will never look at once you automate it and just use whatever Excel file they were using before). YMMV. Very PM dependent.
Central alt data teams sometimes "graduate" ppl onto investment pods. Otherwise complaints I've heard is that you feel outsourced and there's a lot of hand wringing about IP and revenue attribution. Upside is you might actually have your technical skillset appreciated by colleagues and mentors.
The general downside / upside of the alt data roles is if you stay in that role too long, you become an "alt data person." Which is good in that you have a very specific skillset and knowledge base which right now you can get well compensated for. The downside of this data is that it's a race to the bottom and you rapidly learn that its not actually the data scientists analyzing it that add value (its not, its a commodity, basic EDA, dig around, clean it, run some queries), it's the ppl who source new datasets. B/c data = edge (until everyone else gets it, then it's just keeping up with the market). And that period where it actually does add edge, many fundamental ppl don't trust it. Until everyone else is using it, then they trust it. And at that point, it adds zero value.
Great way/role to get your foot in the HF door as a data science person without a PhD though.
Extremely insightful response; with my foot in the door, where would I go? Seems like you think data sourcing is a good path; is there an ability to move into quant research? Generic analyst positions?
I am a data person at a LS fund. If I could give my former self one piece of advice it would be to try to shoot for macro rather than LS. The path from data guy to risk taker there is more established and is basically one of two main pipelines for sourcing talent (the larger one being trading on the sellside). In LS it happens too but you need to be more proactive. Also maybe this is a grass is greener mentality but I get the sense that LS is sweatier than macro.
Out of interest are you in large MM type shop, or smaller SM?
Repellendus repellat natus perspiciatis dolores repellat et exercitationem. Nemo voluptatem fugit repellendus sapiente voluptatem incidunt nemo aut. Suscipit corrupti itaque et commodi est voluptate minima. Unde facilis reprehenderit architecto a omnis.
See All Comments - 100% Free
WSO depends on everyone being able to pitch in when they know something. Unlock with your email and get bonus: 6 financial modeling lessons free ($199 value)
or Unlock with your social account...