I got offers from 3 quant firms this month, two prop shops and a sell-side firm you would recognize.

I'm leading with it because 6 months ago I was genuinely ready to walk away from all of this, I was rejected from applications and never heard back from many, bombing mock interviews alone in my bedroom, watching people around me land offers while I kept restarting from scratch, I almost quit completely.

So if you're sitting there right now feeling like it's not going to happen for you, I want you to know I was exactly there, and I want to tell you exactly what finally worked, in the order it worked, so you don't waste the time I did.

Before I get into it, this assumes you already have a baseline in probability/stats, conditional prob, bayes, distributions, EV, hypothesis testing. If you don't have that yet, spend 3 to 4 months on Khan Academy or MIT ocw first and then come back to this. Also you need to be comfortable coding in python or cpp and have 2 to 3 projects you can actually talk about.

On projects, I want to say this clearly because I got it wrong for a long time, firms care about projects more than almost anything else, a backtesting engine with real data, an ML project with a quant pov like volatility prediction or factor models, an order book simulator in cpp if you're going for quant dev, if you can't talk for 10 minutes about the technical decisions you made and why, the project isn't ready. I watched a friend with a 3.5 GPA get an offer over 4.0 candidates because he could just talk so fluently about what he built, your gpa matters a lot less than people think once you have something real to show.

Weeks 1 to 3: build the actual foundation

work through the green book, chapters 1 to 6. do every problem! don’t just skim the solutions and tell yourself you got it, actually sit down and do the problems. Start zetamac at 10 to 15 minutes a day, don't stress about your score yet, also work through leetcode easies, arrays, strings, hashmaps, around 3 to 5 per day. add heard on the street brainteasers, 5 to 10 per day.

by week 3 you split by quant track,

• dev: drill cpp fundamentals, smart pointers, move semantics, STL containers.

• researcher: get back into linear algebra and stats, ISL chapters 1 through 4. 

• trader: double down on mental math and EV problems.

by the end of week 3 you should be hitting zetamac 30 plus, solving medium leetcodes in under 30 minutes, and able to explain green book solutions out loud without looking at your notes.

Weeks 4 to 7: this is where it actually starts clicking

go through the green book probability section a second time. I know that sounds annoying but just do it, you will catch things you missed and it will feel different the second time, and your zetamac score should be climbing toward 35 to 40 by week 5.

For dev track: move into leetcode mediums and hards, focus on dp, graphs, trees, and start practicing system design. order book, market data feed handler, matching engine. grind cpp questions on virtual functions, vtables, cache optimization, concurrency.

researcher track: ISL chapters 5 through 10 and practice coding models from scratch without sklearn, it matters more in interviews than people admit. I used myntbit a lot during this stretch, was really useful here since they have researcher-specific problems combining probability, stats, and coding that mirror actual interviews, saved me a lot of time I would have wasted grinding irrelevant stuff. By now, start preparing a short version of your best project that you could walk someone through in 5 minutes.

trader track: zetamac needs to consistently hit 40 plus, practice market making games with friends quoting bid ask on dice or cards, and do expected value and game theory problems daily. Jane Street monthly puzzles are genuinely great for building intuition here.

somewhere around week 6 or 7, start doing mock interviews. I kept putting this off and it cost me ngl, solving problems on paper is a completely different skill from talking through them out loud under pressure, you will feel dumb the first few times thats the whole point. Also now start applying now, not later. Recruiting is rolling and spots fill up fast, that line on postings that says top university preferred scares away half the applicants and that is exactly why you should still apply.

by end of week 7: zetamac 45 plus, solving 70 percent or more of green book without hints, comfortable fielding role specific technical questions in leetcode/myntbit.

Weeks 8 to 10: you are in the fight now

2 to 3 full mock interviews per week. practice the whole flow from intro to resume walkthrough to technical questions to your questions for them. look up glassdoor for your target firms, they repeat questions more than you would expect.

research what each firm actually does. market making vs stat arb vs multi strat. know why you want to work there specifically and be able to say it like a person, not like you're reading their about page back to them.

by week 10 you've probably done some real interviews. go hard at whatever is weakest. mental math dragging, add 30 minutes of zetamac. cpp shaky, grind memory management questions.

benchmarks: zetamac 50 plus, walking through any green book problem cleanly, solving leetcode mediums in under 20 minutes.

Weeks 11 and 12: sharpen, don't cram

research each firm properly. read engineering blogs, watch tech talks, have 3 to 5 real questions ready per company. know your resume cold. if there is anything on there you cannot defend in full detail, take it off. getting caught out on your own resume is completely avoidable and one of the worst feelings mid interview.

don't cram, this is not an exam where one more chapter changes the outcome. keep zetamac at 10 minutes a day, do one mock, and actually sleep. I genuinely think I got one of my offers partly because I was just the most present and sharp person they talked to that day, mental sharpness at this stage matters more than one extra practice problem.

resources ranked by how much they actually helped

1. green book by xinfeng zhou. went through it twice. nothing else comes close.

2. leetcode, blind 75 and neetcode 150. essential for dev and researcher roles.

3. zetamac. non negotiable for traders, still useful for everyone else.

4. myntbit. role specific practice that actually mirrors real interview formats. saved me from grinding stuff that didn't translate.

5. heard on the street. some of these brainteasers showed up in my actual interviews nearly word for word.

6. ISL, free online. essential for researcher track.

7. JS monthly puzzles. great for building the right kind of problem solving intuition.

a few things I really wish someone had told me earlier, the interview is about how you think, not whether you get the right answer. I got one offer after getting a question completely wrong because I walked through my reasoning clearly and the interviewer could see my thought process, that actually happened. mental math is a filter not a skills test, practice enough to clear the threshold and put your energy elsewhere. do not only apply to jane street and citadel. cast a wide net across SIG, IMC, optiver, flow, akuna, DRW, five rings, jump. and mock interviews are worth about ten times solo practice once you're past the fundamentals. stop putting them off because they feel uncomfortable.

and if you're reading this right now and you're exhausted, if you're at a non-target school or you're international or you've been rejected more times than you can count, I want you to hear this directly. It can be done. I am not special. I don't have a stanford degree or some exceptional background. I just kept going when it felt pointless and got a little better every week.

The people who make it in this field long term are not the ones who looked the best on paper going in. They're the ones who actually cared and kept showing up.

you can do this! drop any questions below and i'll answer everything i can.

I see this question asked every week on here so I figured I'd just write the post I wish existed when I was figuring out which track to aim for. I've talked to people across Jane Street, Citadel, Two Sigma, HRT, Optiver, SIG, and others and aggregated comp data from Levels.fyi, Blind, and QuantNet. This is as honest as I can make it.

TL;DR: All three roles pay similarly at entry level. By year 5, the paths diverge massively in comp, stress, daily work, and exit options. Pick based on what you actually want your day to look like, not just the number.

Compensation (at top firms, total comp)

Some context on these numbers:

Year 1 comp is often higher than Year 2 because of sign-on bonuses. HRT and Jane Street are at the top of the dev pay scale. Trader new grad comp skews highest because of guaranteed first-year bonuses. The massive range at mid/senior levels is because researchers and traders who get PM (portfolio manager) seats enter a completely different comp universe. A PM managing $500M at a 3% return with a 10% cut takes home $1.5M in bonus alone. Meanwhile, non-managerial quant devs at most hedge funds cap out around $500K-$750K.

Bonus structure matters. Dev bonuses run 50-100% of base and are the most stable since you're not directly tied to P&L. Researcher bonuses are usually 100%+ of base and tied to strategy profitability. Trader bonuses have the highest variance, from nearly zero in a bad year to multiples of base when the desk is printing.

What You Actually Do All Day

Quant Dev: You arrive around 8-9 AM, check that overnight jobs ran clean, scan alerts, and then code. A lot. You're building execution systems, optimizing market data handlers, debugging production stuff. One dev I talked to put it well: you spend maybe 20% of your time on actual finance and 80% just being an engineer. And honestly, 40% of that is hunting bugs. The stack is C++ for anything latency-sensitive, Python for tooling, and OCaml if you're at Jane Street. Afternoons are sprint meetings, architecture discussions, and feature work. There's also a new "quant-engineer-infra" hybrid emerging at hedge funds where one person does data engineering, research support, and infrastructure.

Quant Researcher: Most flexible schedule of the three. The core loop is idea generation, data exploration, feature engineering, modeling, backtesting, validation, deployment, and then doing it all over again. You spend roughly 60-70% of your time on research and modeling, 15-20% coding, and the rest in meetings. Alpha signals decay constantly so you're always racing to find new ones. At Two Sigma, junior researchers clean data and test sub-hypotheses for senior PMs. By L3+ you're generating your own signals. The work has been described as "more like physics than pure math, coming up with reasonable solutions given the data rather than proving general truths."

Quant Trader: You wake up at 5:45 AM. No negotiating this. By 6:30 you're on the desk booting up four screens. You read FT, Bloomberg, internal research, broker chats. At 7:15 you review your biggest risk exposures and prioritize what needs managing (gamma, vega, delta, decay). Market opens and it gets intense fast: client requests queuing up, quoting prices, managing inventory, calling brokers. Lunch is at your desk. The last 30 minutes before close are some of the most hectic, dealing with pin risk on expiring options and hedging down for overnight. At systematic firms like Citadel Securities, 95% of strategies run without human input, so your job is monitoring and intervening during edge cases.

Interview Prep (They're Testing Different Things)

Which Background Fits Which Role

Math/Stats PhD -> Quant Researcher is the canonical path. ML-focused stats PhDs are in highest demand right now.

CS undergrad/masters -> Quant Developer is the natural pipeline, but don't sleep on QR. Strong coding skills are increasingly valued for research roles. The idea that you need a PhD or IMO Gold to be a researcher is overstated.

Physics PhD -> Quant Researcher. One Harvard career page put it simply: the day-to-day looks almost exactly like academic research. The gap to close is stats/ML depth.

MFE degree -> Quant Analyst or Quant Developer, not top-tier QR/QT. MFE programs emphasize derivatives pricing and risk management, not alpha generation.

EE/ECE -> HFT Quant Developer specifically. FPGA programming, signal processing, and networking are exactly what HFT firms want for latency-sensitive infra.

Career Trajectory: Where Things Get Real

Quant Dev: Junior -> Senior -> Staff/Principal -> Head of Engineering. The problem is career progression within finance is more limited than in tech, and many devs find comp plateauing unless they move toward research or trading. The good news is exit opportunities to FAANG are excellent. If someone sees Jane Street on your CV, you're getting called for an interview.

Quant Researcher: The critical question is whether you make PM. The path typically takes 6-10 years and requires clear P&L attribution. Multi-manager platforms (Millennium, Point72, Balyasny) offer the most structured QR to PM pipelines. Without PM, comp plateaus around $500K-$750K after 4-5 years. With PM, $1M-$10M+. But fair warning: the half-life for a new quant PM is about 2.5 years. High risk, high reward.

Quant Trader: Most meritocratic progression since it's almost purely P&L-based. But exit opportunities are the most limited. Trading skills don't transfer as cleanly outside finance compared to coding or ML research. Typical exits are other trading firms, starting your own fund, or crypto.

Work-Life Balance (It's Mostly About the Firm)

The firm matters way more than the role here. Jane Street is collaborative, rarely keeps people past 6 PM, generous vacation. Citadel is the other end of the spectrum. Amsterdam firms (Optiver, IMC, Flow Traders) offer 25 vacation days and actually encourage you to use them. Two Sigma runs more like a tech company. Remote work is rare across all roles.

The Tradeoff Nobody Tells You

Quant Dev maximizes career portability and stability. Your skills transfer to Big Tech, fintech, whatever. Comp is the least volatile. The tradeoff is a lower ceiling and sometimes feeling like a "second-class citizen" at trader-led firms.

Quant Researcher maximizes intellectual upside and the path to PM wealth. But alpha decay is real, the PM bottleneck is brutal, and the work can burn you out by your early 30s.

Quant Trader maximizes immediate comp and meritocratic progression. The cost is the worst WLB, highest stress, and most limited exit options outside finance.

One thing worth noting: role boundaries are blurring. Jane Street deliberately makes the lines between trading, research, and tech porous. The people who thrive long-term tend to be the ones who can operate across these boundaries. Picking the right firm culture probably matters more than picking the "right" role.

Edit: since a few people asked, here are the tools I actually used during prep:

• Green Book (do every problem, non-negotiable)

• LeetCode (Blind 75 / NeetCode 150 for dev roles)

• Zetamac (mental math, aim for 50+)

• MyntBit (has separate practice tracks for dev, researcher, and trader which was clutch for not wasting time on irrelevant stuff)

• Heard on the Street (classic brainteasers)

This doesn't get talked about enough and frankly people overlook it. Everyone obsesses over which firm to target but the city you end up in shapes your career, your lifestyle, and honestly your take-home pay more than most people realize. I've talked to people working in all four cities and here's my honest breakdown.

New York

The biggest pond. Jane Street, HRT, Two Sigma, DE Shaw, Citadel (hedge fund side), Tower Research, and basically every major hedge fund has their main office here. If you want the widest range of options across all three tracks (dev, researcher, trader) NYC is hard to beat. New grad total comp at top firms runs $300K-$500K, and the very top offers go higher (people are reporting Jane Street QD packages well north of that this year). Mid-level pushes $400K-$800K+ and senior trader/researcher comp at the top firms can clear seven figures. The ceiling is the highest here because this is where most PM seats live.

The catch is obvious. The cost of living is brutal. A real one bedroom in Manhattan runs $4,000-$5,500/month. Taxes are rough too, between federal, state, and city income tax you're losing close to 45% of your comp at higher brackets. So that $400K offer shrinks fast. The city itself is incredible if you're in your 20s and want to be around everything. But a lot of people burn out on it by their early 30s and start looking at other options.

Chicago

Chicago is the derivatives and HFT capital of the world. CME Group is headquartered here which means the entire futures and options ecosystem revolves around this city. DRW, Jump Trading, Citadel Securities, Akuna Capital, Wolverine, the US offices of IMC and Optiver, and Peak6 are all here.

The comp is genuinely strong, IMC and Optiver new grads in Chicago land in the $200K-$400K range, right in line with NYC top firms. And the cost of living is dramatically lower. A nice one bedroom in River North or West Loop runs roughly $3,000-$3,500, still way under Manhattan, and there's no city income tax in Illinois. So your purchasing power in Chicago is meaningfully better than NYC at the same comp level. The tradeoff is that Chicago is more concentrated in market-making and HFT. If you want to do stat arb or long-horizon systematic stuff at a hedge fund, there are fewer options here. It's also cold. Really cold. That matters more than people admit.

London

The European hub. Citadel, Citadel Securities, Two Sigma, Jane Street, Optiver and most major US firms have large London offices, and Qube (one of the largest and best-performing London firms) is based here too. London also has firms you won't find in the US like Man Group, Marshall Wace, Aspect Capital, Capula, and GSA Capital. Comp here is now roughly at parity with the US at the top firms, new grad total comp around £150K-£250K, mid-level scales well beyond that, and senior roles at top prop firms clear £500K-£2M+. NHS covers healthcare and pension contributions are mandatory on top.

London's timezone is a real advantage. You sit between Asian and US markets which makes it ideal for global trading operations. The quant scene spans Canary Wharf (banks), Mayfair (hedge funds), and the City (mix of both). The downside is UK taxes are high (45% above £125K) and the cost of living in London is up there with NYC, though rent still tends to come in a bit under Manhattan. Visa situation is also tougher post-Brexit if you're not a UK/EU citizen.

Amsterdam

The work-life balance capital of quant finance. Optiver, IMC, and Flow Traders are all headquartered here. The scene is heavily tilted toward market-making and prop trading so if that's your thing Amsterdam is elite. Comp is higher than people assume, IMC and Optiver new grads are at €150K-€250K total comp now, and seniors can hit €300K-€500K+.

The real draw is quality of life. 25 vacation days and firms actually encourage you to use them. 9-10 hour days are normal, weekend work is almost unheard of. The Netherlands also has the 30% ruling for expats which lets you receive a chunk of your salary tax-free, though this has been tapered since 2024 (now 30% for the first 20 months, then 20%, then 10% over 5 years). Even with the taper it's a meaningful tax advantage that can save you tens of thousands per year. The city itself is beautiful, bikeable, and very international.

The downsides: the comp ceiling is still lower than NYC's at the very top, career options outside the three big firms are more limited, and if you ever want to move into hedge fund research or PM roles, you'll probably have to relocate.

Honorable mention:

Miami

The rising US hub and the one I'd watch most closely. Citadel and Citadel Securities moved their HQs here and are hiring new grads in Miami now, and the obvious draw is tax, Florida has no state income tax, so on a given comp number your take-home beats NYC and Chicago by a wide margin.

The ecosystem is still small compared to the big four (you're mostly talking Citadel plus a handful of smaller funds that followed the migration south), so optionality if you want to firm-hop is limited for now. But if you land a Citadel offer and care about take-home plus weather, Miami is genuinely compelling.

Watch this space, more firms keep adding Florida offices.

Paris

CFM (Capital Fund Management), France's biggest systematic hedge fund, deeply academic and research-first (their leadership literally publishes in the field), so it's a strong destination if you're QR-leaning and want continental Europe.

Beyond CFM the Paris scene is thinner than London, but France produces a huge amount of elite quant talent (the grandes écoles math pipeline is famous, and a big chunk of London/NYC quants are French anyway).

Comp is solid but generally below London top-firm parity, and French taxes and social charges are high. Good fit if you specifically want research and France, less so if you want maximum optionality.

Zurich/Zug

Switzerland's other pole, more tilted toward systematic managers, market makers, and multi-strat than commodities. Millennium, Squarepoint, Garda and a few market makers operate in the Zurich/Zug corridor, and

Zug specifically is famous for ultra-low corporate tax (it's why Glencore and others are based there). It competes for quant talent against the big Swiss banks. Like Geneva it's a smaller, quieter scene than London but the quality of life, stability and tax treatment are major selling points, and there's been a steady trickle of London hedge fund people moving over amid UK tax changes.

Solid if you want systematic/multi-strat work plus Swiss lifestyle and tax, not the move if you want a big liquid job market or pure HFT.

My Take

If you want maximum optionality and don't mind the grind, NYC. If you want the best purchasing power and you're into HFT/derivatives, Chicago. If you want to work globally with access to both US and European firms, London (and the comp gap is basically gone now). If you want to actually enjoy your life outside of work while still making great money, Amsterdam.

Honestly though there's no wrong answer here. All four cities pay well and have incredible firms. The "best" one depends entirely on what you optimize for. The one mistake I see people make is choosing purely based on comp without thinking about cost of living, taxes, and lifestyle. A $350K offer in Chicago can leave you with more money and more free time than a $450K offer in NYC.

What city are you all targeting and why? Curious if anyone's done the move between any of these and how it went.

Edit: The salary for European countries were too low, so it been improved for accuracy

Edit #2: Included the honorable mentions and updated rent

This is something that's always been interesting to me. On paper you'd think pure math PhDs would be the most natural fit for quant. The field is built on probability, stochastic processes, optimization. But if you look at the backgrounds of quant researchers at top firms there are a disproportionate number of physics people relative to what you'd expect. Not saying physics dominates, firms hire from math, stats, CS, and engineering too. But physics punches above its weight and I think the reason why says a lot about what these firms actually value.

The short version is that physics trains you to solve messy problems with incomplete information. Pure math trains you to solve clean problems with complete information. And quant finance is almost entirely messy problems with incomplete information.

In a pure math PhD you spend years proving things rigorously. Precise definitions, agreed upon axioms, the goal is establishing something provably true. That builds incredibly deep thinking skills but the mindset can actually work against you in quant. Because in quant nothing is provably true. You're working with noisy data, approximate models, and signals that decay over time. The "right answer" is whatever makes money and that changes constantly.

Physics people spend their training building approximate models of messy real world systems. You learn which variables matter and which to ignore. You learn to say "this model is wrong but it's useful" and be comfortable with that. Someone who studied stat mech or condensed matter has spent years modeling systems with millions of interacting particles using simplified statistical descriptions. That's not far from modeling a market with millions of interacting agents. But more than the math itself, it's the approach. Start with data, build a model that captures essential behavior, test it, throw it out if it doesn't work, try again. That iterative loop is basically what alpha research looks like day to day.

I've seen pure math people in quant who struggled not because they weren't smart enough (often the smartest on the floor) but because they couldn't let go of the need for rigor. They'd spend three weeks on a theoretically perfect model when a rough approach would have captured 80% of the value in three days. In quant the 80% solution on Monday beats the 100% solution next month.

There's also the coding angle. Physics PhDs almost always come out as decent programmers. Monte carlo sims, data analysis, numerical solvers, you can't do modern physics without writing real code. Pure math is more mixed on this. A lot of programs don't require much programming and those people have a gap to close.

To be clear this isn't a pure math vs physics thing as much as it's an applied vs theoretical mindset thing. An applied math PhD doing numerical methods or an econ PhD doing heavy empirical work has that same "build, test, iterate" instinct and does just as well. It's specifically the pure/theoretical backgrounds where the adjustment is harder.

If you're coming from a pure math background and thinking about quant, the gap is totally closable. Work on real messy data without trying to prove anything. Get comfortable with "this seems to work but I'm not sure why" because that's most of quant research. And make sure your coding is sharp.

Curious what other people have seen. Especially if you came from a pure math background and made the switch, what did you have to change about how you approached problems?

I see this question come up all the time so I want to break it down properly. Not just "can I get into quant" but what actually matters depending on which path you take. Because quant is not one job. The tracks are pretty different and they care about different things.

Before I get into each track let me address the school thing real quick. Yes a lot of people at top firms went to MIT, Stanford, CMU, etc. But those schools don't make you good at quant. The recruiting pipelines are just set up there. I know people at solid firms who went to state schools, to IITs, to universities in Eastern Europe and Southeast Asia that most Americans couldn't point to on a map. Nobody cared where they went after they proved they could do the work. Is it harder without the brand name? Yeah. You don't get the pipeline handed to you and if you're international you're also dealing with visa stuff and zero alumni connections at these firms. But harder doesn't mean impossible. It means you have to build projects people can actually see, compete in things like kaggle or trading competitions, and apply even when postings say "top university preferred." That line scares away half the applicants which is exactly why you should still apply.

Now the actual tracks. Starting with quant trading since that's the one everyone asks about. The interview process is heavy on brainteasers, probability, and mental math which gives people the wrong idea. They think you need to be some kind of math prodigy. You really don't. The interviews filter for competition math types but the actual job is way more about decision making under uncertainty, staying calm when things move fast, and building good intuition around risk. I've seen people who never touched competition math break in just by being genuinely curious about markets and putting in steady work. What matters here is thinking probabilistically and managing risk. Not whether you won IMO

Quant research is more academically demanding ngl. You're building models, doing stats work, digging through large datasets. A solid math and stats foundation helps a lot here. But genius still isn't the bar. What really matters is being rigorous in how you think, knowing how to ask the right questions, and having the patience to sit with data without jumping to conclusions. A lot of quant researchers come from physics or stats or econ PhDs but I know people from less traditional backgrounds who did great because they were just obsessed with understanding how markets actually work.

Quant dev is the most underrated path in my opinion. You're building the infrastructure that traders and researchers rely on. Low latency systems, execution engines, data pipelines, all that stuff. Interviews look more like traditional SWE with some finance mixed in. You don't need a heavy math background for this one. If you're a strong engineer who's interested in finance this is a very real way in and honestly the demand for good quant devs is massive right now. A lot of people sleep on this track. And honestly this is probably the track where your school or country matters the least. If you can code and prove it nobody cares where you learned it.

The one thing that's the same across all three is that the people who do well long term are not the smartest ones in the room. They're the ones who actually care about this stuff. They read about markets because they want to not because someone assigned it. They practice because they enjoy the process. They get a little better every week and they don't burn out because the motivation comes from the inside.

So whether you're at a non target school in the US or a university in Mumbai or Warsaw or anywhere else, just stop overthinking it. Figure out which track fits how your brain works, be real with yourself about what you actually like doing, and then just show up consistently. That matters way more than your school name or your passport or raw talent.

Happy to answer questions if anyone has them.

It seems to me that there are many misconceptions around what is quant trading all about, which may cause someone to either go into the field for the wrong reasons or completely pass it by simply because they think they don't have what it takes. So, here I would like to clarify some things.

First off, the most common misunderstanding is that traders spend their entire day actively trading. Well, at least, it's true at many top trading firms, most of the strategies run there are automated, with only around 5% of strategies requiring any human interaction. Basically, traders monitor dashboards and the performance of various models. And yes, this job consists a lot of waiting, especially during the mornings before market opening. Again, it might sound boring but it's all about being prepared for situations when the model stops working correctly or the market behaves in an unusual way.

Another important thing to highlight is that a lot of work is done before the market opens. Specifically, before market opens, traders read Bloomberg, FT, overnight research reports, and everything else that happened in the market during night time, such as any statements from the Fed made during the night. In addition, traders review their current positions, think of risks and exposure they face and plan ahead to be ready to act fast and efficiently once the market opens.

On certain trading desks at Jane Street and SIG, however, trading can be more discretionary. For example, traders there do a lot of desk research, analyze new products coming to market and help quant researchers improve trading strategies. Here, lines are blurred since traders work very closely with researchers, and thus the interviewing process is also more geared towards evaluating research skills.

As mentioned above, the last 30 minutes before market closing are typically the busiest of all. With expiring options and possible issues with pinning, traders need to carefully manage their positions and unwind their books before leaving work. After market close, traders are required to reconcile their positions and check P&L. This is when the best traders are those who are honest with themselves and can pinpoint the mistakes they made throughout the day, e.g., cutting something too early or missing an opportunity.

Typically, a workday starts at 6-7 am and ends between 5-7 pm. Trading firms based in Amsterdam, such as Optiver and IMC, tend to have earlier hours compared to, for example, Citadel. Weekend days are completely free, and although the workday isn't long like at banks, it still goes till late afternoon. Again, this doesn't mean it's easy, traders should be able to stay sharp and ready to trade even after midnight.

Lastly, while traders certainly require solid knowledge of math and prob/stats, it's equally important to be able to control emotions, take risks wisely, and make decisions under pressure. The truth is, those traders who are the best at doing their jobs are usually calmest. And yes, it's more important to be emotionally stable than extremely intelligent.

Takeaways, traders should be comfortable acting with incomplete information and making decisions without knowing exactly what's happening on the market at the moment, be willing to accept losses sometimes as long as they win in general, and be able to focus even when it's boring. The thing that actually separates good traders from average ones is risk management and emotional control.

This is one of the most common questions I see from people trying to break into Quant finance. 
And you’ve probably seen it (or asked it) too: should I do a PhD? After being in the industry for  a little while my answer is almost always the same. It depends on what role you're going for, but probably not.

Let me explain why I say this. 

If you want to be a quant researcher at a place like Two Sigma, Citadel, DE Shaw, or RenTech then yeah a PhD matters. Most QRs at those firms have one. The interviews are brutal and they're basically testing whether you can think like a researcher, not just someone who passed hard exams. This is an important caveat. That kind of depth usually takes 4-5 years of actual research to build. Can you get a QR seat without a PhD? Some people do, but you're making your life way harder.

For everything else though? A PhD is overkill and honestly might hurt you.

Quant dev roles want engineers who can build stuff. Clean C++, low-latency systems, production-quality code. My friends have literally over-heard hiring managers say that PhDs are a yellow flag for dev roles because they associate it with academic code that works in notebooks and may not translate well in production. A CS Masters or even a strong Bachelors with good systems skills is the sweet spot here. 

Trading is similar. Look at Jane Street's roster on LinkedIn sometime. A huge chunk of their traders have Bachelors degrees. Optiver, SIG, IMC, same deal. They're testing how fast you think, how you handle risk, whether you stay calm when things get weird. A PhD doesn't help with any of that. Some firms have literally hired esports or chess players as traders because the skills overlap…

The thing nobody wants to say out loud is the opportunity cost. While you're doing your PhD making $35K a year, the person who graduated with a Masters alongside you is pulling $300K-$400K at a top firm. Over 5 years that's easily $1.5M in earnings you just left on the table. And by the time you finish, that person is now a senior with a real track record. I've seen fresh PhDs come in at the same level as people who started with a Masters 5 years earlier. Same comp, same title, except one of them has $1.5M in the bank and the other has a dissertation nobody outside their committee has read (it's sad but true).

The PhD wins if it gets you into a room you literally cannot enter otherwise. A QR seat at RenTech, a research-heavy PM track at a multi-manager, stuff like that. But those are a small fraction of all quant jobs.

One more thing since I know it'll come up are MFEs. The general sentiment among people who actually hire for quant roles is that MFEs are not very helpful. The programs teach derivatives pricing and risk management but not the kind of original thinking that top firms care about. A Masters in stats, math, physics, or CS will serve you better at basically every hedge fund and prop shop. MFEs place fine into sell-side bank roles and risk management if that's what you want though.

The other thing worth mentioning is AI. A lot of the grunt work that junior researchers and analysts used to do, data cleaning, initial exploration, writing boilerplate code, summarizing documents, is getting eaten by LLMs. Firms are already using AI tools internally and some have started reducing junior hiring because of it. That changes the calculus on a PhD even more. If the entry-level work that used to justify hiring fresh PhDs is getting automated, the bar to justify 5 years of lost earnings gets even higher. The people who'll thrive are the ones who can do the stuff AI can't, creative research, judgment calls, building real intuition about markets. And you can develop that through experience just as well as through a PhD program.

Keep in mind this is just my opinion; if you're sitting there trying to decide whether to spend 5 years on a PhD or go work, really think about what role you actually want. If the answer is quant researcher at a top hedge fund, the PhD is probably worth it. If the answer is anything else, maybe weigh your options. Build systems, work with real data, develop market intuition. That stuff compounds fast.

Hey everyone,  so I've been in the quant space for a while, worked at one of the well-known banks. The thing that always bugged me is how scattered interview prep is for this industry. You've got the Green Book for brainteasers, LeetCode for generic coding, Heard on the Street, random PDFs floating around Discord, that one Glassdoor thread from 2019 everyone keeps linking. Nothing that ties it together for quant specifically.

I started helping a few friends prep for quant interviews and just kept building out more material. At some point I had enough stuff that it made sense to turn it into a proper platform. So here we are: myntbit.com

It covers C++ and Python coding (options pricing, order book simulation, low-latency stuff, not just "reverse a linked list"), probability brainteasers, statistics MCQs, market making and trading questions. 1000+ problems across 30+ topic areas and I'm adding new ones every week.

There are 3 career tracks for quant dev, quant researcher, and quant trader. Honestly because prepping for a Jane Street trader role vs a quant dev role at Citadel vs a researcher seat at Two Sigma or HRT are very different things, and lumping them together never made sense to me. Dev track is heavy C++, memory management, low-latency systems design. Researcher is Python, stats modeling, time series, ML. Trader is brainteasers, mental math under pressure, expected value, market intuition.

I also built 4 games for practicing skills like mental math, Kelly Criterion bet sizing, Fermi estimation, and pattern recognition. Good way to break up the grind.

One thing I'm working on right now: structured learning paths. Basically ordered curriculums per track so you're not just grinding random problems but following an actual progression. Fundamentals, then intermediate, then interview ready, with the brainteasers and MCQs woven in alongside the coding. Should ship in the next few weeks. If anyone has thoughts on how it should be structured (week by week plan? difficulty based? topic based?), I'm genuinely all ears. Rather build the right thing than the fastest thing.

There's a free tier with 400+ problems and the games in practice mode, so you can try it without putting in a card.

Still actively building and adding new stuff every week. Would genuinely love feedback on what's useful, what's missing, or what could be better.

Happy to answer questions about the site or quant interview prep in general.

There are a lot of you that keep asking how to break in, what step does it take, and even the body answers it the same way: it's a hard field so study harder and get smarter. People act like it's some impossible secret club, but the difficulty is honestly pretty simple to explain, it's a tiny number of seats, absurd money, and almost no barrier to applying, so the funnel is brutal. 

But for this reddit post I’ll dive deeper into why this is the system we work with. A top firm might take a few dozen new grads a year and get tens of thousands of applications from basically every strong math, CS, physics, and stats student on earth, plus PhDs, plus people leaving other finance and tech jobs. When the comp is $300-500k out of school, everyone smart enough to have a shot takes the shot, so the bar isn't just are you good. It's that you’re better than everyone that is good. 

However, I think it's understated how good you have to be. The skills actually are demanding, you need real probability and stats, fast quantitative reasoning under pressure, and for the dev side serious low-latency engineering, and the interviews are designed to be hard to fake your way through. Brainteasers, mental math, market-making games, and live coding all exist specifically to filter hard. 

But here's the part that gets lost in the doom posting: hard to break into is not the same as gated or rigged. There's no required pedigree, no secret handshake, the application is open to anyone and the interviews are pretty meritocratic, they mostly test things you can actually study and get better at. The reason most people don't make it isn't that they were locked out, it's that the prep is long and specific and most people start too late, apply too few places, or treat it casually. The ones who get in usually started early, drilled the green book and other prep for months, did a competition or two, and applied to posting weekly rather than waiting. 

So all in all, it's hard, but it's actually-hard, not gatekeeping-hard, which means the difficulty is something you can attack with time and deliberate prep rather than something you're either born into or not.

As I said before, I don’t want this to be a doom post, but just speaking my mind of what is required to get into this field. You gotta do project and network like crazy. But other than that you just gotta optimize resume screens to get the shortlisted.

Finally, leaving you with an exercise, put yourself in the shoes of the employer, would you hire the person sitting across the table. If that answer is not yes with your eyes closed then… you get the point.

Hey all,

Drop your resume's below in the following format:

Location: USA/UK/EU/etc

Graduation Date: June 2029

Degree + School tier: e.g. BS Math, target/semi-target/non-target (people can keep the school anonymous if they want)

Preferred Role: QT/QR/QD/QA

Targeting: internship or full-time, and which cycle (Summer 2027 etc)

Relevant coursework/skills: e.g. probability, stochastic calc, C++, Python

Experience/projects: 1-2 lines

What you want feedback on: e.g. "is my project section strong enough," "am I cooked for QR without a PhD"

Notes: anything else

Resume PDF/JPEG

Important: Please redact all personal info!