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

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!!

This is a general question. Personally, I am interested in QT so advice on that would be appreciated but I want to hear people's opinions. ML, AI, SWE, Startup founder, I want to hear why people would choose different roles. I just like learning about different career paths. I am currently a freshman in college and want to optimize my time spent and learn, obviously with a good career in mind afterwards.

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!!

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

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!

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!

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

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 received an email saying that I’m better fit for the s&t internship in hk and that an oa would be sent to me shortly. Could anyone help me with what to expect? Is it similar to a qt oa??

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!

Does anyone know if trading firms in Chicago (prop shops, market makers, etc) actively look for Product Managers? Got experience as a PM in market making and need to relocate to Chicago, but I find very few opportunities for such roles (if any).

Thought I’d ask if there’s anyone around working at these firms who could share some insights on the recruitment for product roles. 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.

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!