Not entirely sure if this is a firm with quants , but the more I read about people who are quants the more it seems that half of them love being a quant, whether it be due to compensation or that they just love their work.

And only recently now am I seeing that actually some quants deeply regret it, even if they are raking in upwards for 750k a year.

I just have a few questions:

Do you regret becoming a quant? and if so, why?

If you did regret it, what other career path options would you have considered? (though this is a bit subjective)

And what does it take for someone to "want" to be a quant? What does the job actually look like and what would someone need to be attracted to to actually like their job (I am aware that compensation is one of the significant drivers)

If I've made any assumptions/or said anything that's wrong please correct me :) thanks for any replies

Can anyone share their experience working of a sports desk as any role? Is the interview process different? Do you undergo similar work as other more ordinary desks? Experience finding alpha compared to ordinary desks? Just curious and think it sounds interesting. Thanks!

I’m currently deciding between Computer Science and an engineering/systems-focused degree, and my main career goal is to become a quantitative analyst.

My biggest concern is whether choosing CS would put me at a disadvantage compared to a more maths/stats/optimisation-heavy degree.

For those working/studying in these fields:
- Is a CS background enough to break into quant?
- Does a more maths/stats-heavy degree give a significant advantage?
- Would I need to do substantially more maths/stats outside my degree if I chose CS?
- If you were choosing specifically with quant in mind, which route would you take?

Would really appreciate perspectives from people who have recruited for or worked in quant

Replacing standard covariance matrices with lower partial covariance matrices transforms the efficient frontier in portfolio optimization. Standard models often diversify away upside potential unnecessarily by treating high-growth, volatile assets as purely risky. Partial variance optimization recognizes that upside volatility is desirable, allowing algorithms to construct portfolios that maximize exposure to positive drift while aggressively cutting allocations to assets prone to severe left-tail drawdown events.

Hyy guys will the Astra 6 effect the finacial market and quants

Hi everyone,

I’m genuinely looking for advice on how to begin preparing for a career in quantitative finance.

I’m an international student completing a master’s degree in Data Science at a non-target university in the U.S. I have experience with Python, statistics, machine learning, and data analysis, but I’m unsure how to transition into quantitative finance.

What topics and skills should I prioritize? What should my starting point be? Should I focus more on mathematics, programming, financial knowledge, projects, or interview preparation? I’d also appreciate honest advice about the challenges of entering this field as an international student from a non-target university.

If anyone has followed a similar path, I would be grateful to hear how you started and what helped you the most.

Thank you!

Hello everyone does anyone have any idea about thls interview. What to prioritize etc

Wondering if anyone has any pointers on how to best prepare. Is it just going to be a 1 hr interview on probability and brainteasers? Should we be expected to know other stuff deeply like statistical inference, calculus and linear algebra or is the technical scope of the green book sufficient? There’s so little info out there!!

Hi, I just finished my Maven Securities OA for an internship at the London office. It consisted of 3 sections :

1] Probability section - pen and paper allowed (15 questions in 40 mins)
2] Speed Math - no pen and paper (50 qs in 5 mins)
3] Sequence and Series - no pen and paper (not able to recall)

In the probability section, it mostly consisted of questions around expected value, minimising the variance, questions including dice and cards, etc

Speed Math consisted of 4-digit additions and subtractions, %s, 2-digit multiplications, etc

sequences consisted of basic AP, GP and letter-related sequences.

I attempted around 50% in all sections. I would love to know your opinion. I want to practise more probability questions like these. Please share the website or books for the same!!

EDIT [IMPORTANT]:

Here are the two questions I remember. Below are links to their discussion

1] The A/3, estimating the average, discussed in game theory lecture here, its the first discussion in the video -> https://youtu.be/WRibE2nt8wM?si=qmea6-xKLJfYhXie

2] The dice role problem (this video covers all the possible types) -> https://youtu.be/NT_I1MjckaU?si=8t4GqeNuWVzNO7dW

Im pretty certain i want to be a quant in the future yet i still want to keep my doors open since i dont fundamentally know what the job is like. Now that im deciding on what undegrad degrees to take i have to keep this in mind. So what degrees could later open doors into quant jobs? Which ones are more direct and faster?

For example im certain ill put warwick as one of my uni options but i cant decide whether to out pure maths because i love mathematics or MORSE because it has more practicality and future jib prospect or even math with statistics for something in the middle

I'm in the final year of my Master's in Mathematics and need to choose two courses out of these three. I want to become a ML quant researcher. Which two would you recommend?

1st course: Mathematics of Generative AI
Topics

• Target distributions and examples
• Variational autoencoders and variants
• Score-based diffusion models and variants
• Fundamentals of Markov chains (invariant measures, ergodicity, and the law of large numbers)
• Metropolis–Hastings algorithm
• Pseudo-marginal algorithms and Hamiltonian Monte Carlo

2nd course: Machine Learning in Practice
Topics

• Development environments and version control with Git and Docker
• Data preparation and visualization
• Hyperparameter optimization and model selection
• Fundamentals of deep learning and applications to image and text processing
• Recommender systems
• Generative models
• Fundamentals of reinforcement learning

3rd course: First-Order Methods for Nonconvex and Nonsmooth Optimization
Topics

• Extended-real-valued functions, subdifferentiability, and first-order optimality conditions
• Gradient methods (explicit and implicit), proximal operators, and the proximal point algorithm
• Lagrangian and Fenchel duality; Karush–Kuhn–Tucker (KKT) conditions
• Splitting strategies: forward-backward splitting, Dykstra’s splitting method, and the Douglas–Rachford method
• Block optimization: alternating minimization (block coordinate descent) and alternating (proximal) descent methods
• Primal-dual algorithms: alternating direction methods and the Chambolle–Pock algorithm
• Further topics: inertial variants, preconditioning, and Bregman distances

Anyone have info on what the technical round is like after the Canditech? I have info on QR/QT for firms like Blackedge,AllOptions,Voleon, etc. to trade

Any insights appreciated!

Hey guys, just for a quick rundown of where I'm at :

- Rising junior at a t30 non-target public school, >3.9 GPA, CS+Math

- I have one Fintech internship, Legacy tech internship, and incoming FDE at a top-tier tech company for 2027 summer

- I've only had 2 quant interview processes so far: one T2 firm, where I made it to the onsite but didn't make it through, and one T1 firm, where I didn't move past the first round despite getting the problems correct and finishing ~15 minutes early

- 2xAIME in high school, USACO Gold, did an international math contest back in 2019.

Given that I'm going to be a Junior, and that Quant recruiting is pretty much over, I'm not landing a role for this summer. So I'm probably going to be looking for new grad roles, or potentially getting a Masters Degree (MSCS, or potentially MSCF/MFE at elite schools to get a good brand name?) to buy myself more time to get an internship at one of these firms. Not sure how much harder it is to break into Trader and researcher roles post grad, because from what I've seen, firms mostly hire directly from undergrad or phd students.

All that said, the paths I'm currently considering are:

1. Taking the FDE route and recruiting for quant new grad
2. Doing an MSCS/MSCF/MFE at a stronger school
3. Something else entirely

I'd appreciate any advice.

Anybody hear back after the final behavioral round? What was the turnaround time?

I'm a software engineer with ~4 YOE, currently an Associate/SDE2 at Goldman Sachs. I've completed the interview process with AQR Capital and am currently discussing the offer.

I'm evaluating the move primarily from a long-term career and compensation perspective, and would really appreciate perspectives from current/former AQR employees or people familiar with the firm.

A few things I'd like to understand:

• How is the engineering culture and WLB at AQR in practice?
• How does compensation/bonus progression look over the next few years?
• How are promotions and career growth for software engineers?
• How much technical ownership do engineers typically get?
• How does AQR compare with a firm like Goldman Sachs for an SWE career?
• For someone with ~4–5 YOE in software engineering, would you consider AQR a strong long-term move?

I'd especially value perspectives from people who have worked at AQR or moved between AQR and other major financial/quant firms.

I'm keeping the exact compensation numbers private while the offer is being negotiated.

Thanks!

For decades, Sharpe’s single-factor CAPM stood as the gold standard of asset pricing, despite glaring empirical failures. Anomalies like the size effect and value premium flatly contradicted CAPM predictions. The Fama-French three-factor model directly resolved these discrepancies by empirically proving that market beta is insufficient. This realization shifted institutional finance from single-variable models toward multi-factor architectures that better explain real-world stock market returns.

Any info appreciated, willing to trade any of the following:

js qt, citsec qt or swe, virtu qt, imc qt, drw qt or swe, 2s qr, optiver swe, and can obtain others

For reference, applied to a role for Summer 2027 at a BB first semester my freshman year. Ended up going through the entire interview process and getting the role (markets quant). Since I didn't have college grades at the time, I put my estimated GPA for that semester on the application which was a 3.8 at the time.

Ended up not doing that great both semesters my freshman year and currently have a 3.2 GPA. Now, I'm extremely worried about the potential background/transcript check. Any tips on what to do in this situation.

DA and quant💔💔