So you want to be a Quant?
I've seen a lot of what I would consider to be misinformation on this forum about what being a quant is like, where is good to work, what is needed to get in, etc etc. I wanted to give my personal thoughts on what I believe to be fact vs. what is fiction.
Why you should listen to what I have to say:
First off, you dont have to. That being said, I do think I am able to provide a perspective that is a bit unique on this forum. During undergrad I was successful during IB recruiting for my junior summer, and landed in a group that most people on this website would consider "ok." During my internship I decided I didn't like the people, and turned it down my return w/ nothing in hand.
My FT recruiting was initially split between RX and Quant. My RX pipeline was looking MUCH better than the latter, but I decided the barrier to entry there was very low, and if I wanted to do Quant I had to do it straight out of school.
I now currently sit in a MM pod (multi manager, not market maker) as "Quant Researcher." I am the first quant to ever come from my school. Having done both Banking and Quant, and recruited for both I think I can help clear up some of the misconceptions that both parties may have.
All this being said, I am far from a senior employee (have been working 2-3 years) and likely am still wrong about a lot fo things. If you want better advice / info, go read posts from these guys:
MMPM, [Tobin's-Q], anonq
I'd love to invite their commentary as well (not all are quant but have good perceptive / have been helpfull imo)
What everything I am going to say applies to:
Everything I say regarding the quant role only applies to fully systematic (no human intervention) quant research. I don't have a valid opinion on Quant Traders, Quantamental, Market Making, Prop, etc because I dont have the firsthand experience. I could tell you what I have heard / been told but this is often much noisier and lower value information.
Common misconceptions / questions:
Do I need a PhD to be in a research role? - No, absolutely not. I use heavy duty(parralelize stuff across 2000+ cores every day to give an idea of compute), read lots of papers, take part in research meetings, etc. I have an undergrad math degree and no relevant internships. This isn't to say you don't have to be technically proficient, as you absolutely do, but this whole notion that you need a Master/PhD/IMO Gold to become a researcher is absurd.
With this comes hot take number one: The average undergrad quant is likely to have better outcomes than the average PhD quant. Part of this is due to selection - PhD quants are often people who couldn't cut it in academia / their chosen field, whereas the undergrads doing this often really want to be here. Smart undergrads also often know they dont know shit and as a result arn't impaired by arrogance, which plagues PhDs.
Do I need to know how to code? - Absolutely yes, but not necessarily in the way you think. If you are gunning for larger shops with a defined recruiting pipeline, you're going to need to leetcode and there is now way around it. However, if you are going to somewhere without a defined pipeline, having projects that demonstrate you know how to manipulate data efficiently (and being able to reproduce these techniques on the spot) will be sufficient in most cases. You will spend more time coding on the job than anything else - its the language you do your work in. An appropriate analogy would be asking an author if they need to know how to type.
What math do I need to know? - This is one where it's more specific to how you trade and where. If you are a sell side boi where you need to do stuff with some explainability and academic grounding, then stochastic calculus will be needed. However, if instead you are focused on make strong predictions and want to make money, then the math needed to understand popular ML techniques is sufficient. This is a vague description, but its the right one. Just like the coding, the math is a tool to make money. You need to understand it well enough to work through and troubleshoot any models you build / use them properly and in a way that makes sense. I have sat down and solved a "hard" math problem exactly one (1) time in my career, and it didn't end up making any money. For people who really want a list, here: Linear Algebra, Differential Calc, Probability Theory, and basic Calc Based Stats (none of that business school garbage).
Is networking important? - Similar to banking, it is one of the most important parts. This is likely a bit of a hot take, so I'll elaborate. Quant finance is a very small world, much smaller than the fundamental L/S one where it already seems like everyone knows each other. While the first gig may come from one of those big pipelines that is impersonal and regimented, every good opportunity comes from knowing a guy who knows a guy. You know how everyone says in theall the best opps come from referrals / never get posted (Tiger Cubs, etc)? It's the same shit in quant. Similarly, there is a HUGE world of opps for new grads outside of the big bureaucratic firms who's names you recognize,, and you only learn about those by digging around and emailing.
Where is a good place to work? - This is entirely up to the individual and depends on a lot of factors. If you care about brand and plan on exiting to SWE in a few years / dont really care about finance, go work for 2sig. But if you are interested in finance, care about knowing the whole process, and wan to do this kind of work for the long haul, I would strongly recommend targeting small teams that manages their whole process (idea generation all the way to execution). And with this, comes another hot take: The best place to start a career as a quant is in a multi manager pod. This is obviously dependent on having a PM who cares about your development and knows how to make money, but this had huge advantages. A user on this site put why this is the case better than I could (replace IB Stat Arb with Multi Manager Pod):
"I think the Stat Arb track at IB's was good because it was small teams that had to do everything. As opposed to now a days if you start at like Two Sigma/Squarepoint/etc then you only see a small piece of the pie. I'll interview people from places like that and the only thing they ever worked on was portfolio optimization or someor two on a really niche dataset."
This is one of the points that I feel most strongly about from my limited experience. The larger the shop your work at, the more silod you become. The average junior at a Multi Manager / Prop Shop / 10 man fund is going to have a hell of a better idea on how to actually make money than almost any junior at 2sig. If you want to run a book someday or be responsible for PnL, I feel this is critical. The difference between vertical siloing (many teams run their own full process independently) and horizontal (many teams each manage one step in the process on their own) is night and day.
What are the exits for quant? - Ask a L/S HF guy what the exits are from their job. It is the exit. If you want a well defined path where you always know where you'll be at T+2yrs, dont be a quant. I would discourage anyone who wants to jump quick from entering this space (unless you flopped your google interview and want to try 2sig). I understand people want some security in case things go belly up / they get forced out of the industry, and to these people I would say industry data science / fintech I guess? It isn't something I think about much and dont ask people either. The best plan B is no plan B.
Since people love recruiting questions, heres a few quick ones:
When does recruiting happen? - Late summer though the end of Fall
How should I apply to firms? - Amass a long lost of companies you would work for. Group them into 3 ranks, 1 - Dream company, 2 - Great company, 3 - Would work for them as a first job. In three "waves" throughout recruiting season, apply to a few companies in each group. This ensures that you get interview practice at each "level" of firm without burning all your chances with dram companies you just weren't ready for / firms that would be layups once you get practice in the start of the recruiting season. It gives you time to practice live, which is key for quant interviews. Remember, you only need to win once. I'm a fan of being pretty polarizing in interviews / avoiding being meek or boring. The median candidate never gets the offer, so sometimes you're better off turning the continuous distribution into a coin flip by being a bit different.
How should I prep for interviews? - Leetcode,, mental math. Leetcode has been beaten to death on how to do it, just google how to study. For brainteasers, look at the green book and heard on the street. Those cover just about everything and whatever you get will likely be some subtle change to one of those - focus more on understanding the thought process behind an answer vs. knowing the answer, as this lets you generalize when you get a curve ball. For mental math, look up Arthur Benjamins book and drill that.
How can I make sure I get a first round? - I had a 100% hit rate for getting the screener by emailing recruiters at the firm / traders asking to talk to them. Applying IB recruiting techniques to non-IB industries often gives absolutely killer results.
I hope this helps - people on this forum have been great to me, and I wanted to give back in some way. More than happy to answer any questions below, and would love to see some of the quants on here provide their perspective.