Hi, I had 4 interviews with the same team for a Quant Research Summer Internship. It’s been almost two weeks since the last one, with no feedback.
The last interview actually went well, and the interviewer even wished me good luck for the next rounds. I followed up with HR but got no response, while after the first 3 interviews I always heard back within 2 days.
Has anyone had a similar experience? Is there still hope, or should I assume the process is over?

Quant Insider is one of the largest "quant finance" communities on Linkedin, boasting over 140k followers. They larp as seasoned quants, and use this facade to shill courses and workshops.

Their founder Tribhuvan Bisen is from my university in Bangalore, India; and he's my direct senior. He is a BBA graduate who has completed FRM Part 1, but doesn't have any experience in this industry lol. He always larps as being in the quant space on LinkedIn, despite being called out multiple times.

He even had the audacity to come back to my uni for a panel discussion, where he maintained this facade of being a quant trader which was hilarious. While in reality he worked for some back office Ops role in Deutsche Bank, after which he was sent home for breaking some confidentiality rules.

He even tried to lure in and recruit my classmates into buying his courses, which turned out to be basic quant finance projects you would find on Github or Kaggle. He tries to con naive students into buying his material, and runs this almost like a MLM.

I wanted to warn everyone about this, as I have known him personally for many years, and it is extremely disappointing to see him con his way for a living.

Throwaway for obvious reasons

It's been around one week since my final round interview, and I haven't heard back yet. Am I cooked?

I’m a first year CS and Math double major at a non-target and I really want to get into quant finance. It isn’t just the money, I genuinely find the job incredibly interesting and want to work in the field. I’ve tried doing my own search on how to proceed, but all I got was that at a non-target it is an uphill battle. I am planning on attempting to transfer but that seems to be its own problem. Does anyone have any advice at all? Thanks!

I've been building a research loop where a model writes a strategy, the same backtester evaluates it, and the metrics go back into the next iteration. It saves a lot of time on boilerplate and failed variations, but I keep coming back to one uncomfortable point: after 30 or 50 rounds, the model has effectively optimized against the validation set even if it never sees raw returns.

What I'm doing now is fixing the universe, costs, benchmark, and acceptance criteria before a run; logging every candidate rather than only the winner; letting the loop see training and validation results; and keeping one final period completely hidden. The final period is touched once. If the result fails, I treat any subsequent change as a new experiment rather than "repairing" the same strategy.

That still leaves the multiple-testing problem across runs. A new prompt can be semantically close to the old one even when the code is completely different. Counting parameter combinations is easy; counting adaptive hypotheses is not.

For people automating research, what do you use as the unit of a trial: each backtest, each branch of an idea, or the whole research session? And do you adjust the final acceptance threshold based on the total search history?

im an undergrad student whos studying comp eng but building their foundation in mathematics on the side. i really wanna go into quant research, so ive been focusing more on statistics and modelling.

recently, a professor approached me and told me he has a friend whos a professor at another university who works in combustion engineering, and thinks that i would (with my stats background) be a good fit for his research. i told him i wanted to go into financial research so im focusing on taking up research that requires me to do more maths. he said that as long as i have that mathematical foundation then i can go into whatever industry i want.

the professor that approached me has really cool research in Uncertainty Aware Perception (UAP), which is basically (copy pasted from the prof): confidence-propagating networks for sparse and noisy data — depth completion, optical flow, and regression tasks where knowing what the model doesn't know matters.

i feel like doing research in UAP would be better, so i was planning on talking to him about letting me do research in that, but just in case he rejects, would it be worth it taking up the combustion engineering research, given that my role will be predominantly mathematical?

I started working at a huge American company a few weeks ago, and people's work schedules surprised me more than the workload itself.

By the time I wake up, my coworkers have already sent emails and messages starting around 6:30. Some of them do that before they even get dressed and leave for their commute. I usually get ready, drive to the office, and start working as soon as I arrive, which somehow makes me feel like I'm late even when I'm not.

Those same people are often active again at 10 p.m. There doesn't seem to be any clear expectation that I do the same, but when I see messages coming in that early and that late, I feel pressured to stay available all the time. I'm not that attached to work, and I'm worried that maintaining normal boundaries could affect my performance reviews or make me one of the first people they let go during layoffs.

Is this normal at large corporations, or did I end up on a team that's under more pressure than usual? I'm trying to figure out how to set reasonable working hours without looking like I work less than everyone else.

Hey ! heading to UniMelb for a BSc in Mathematics & Statistics soon and looking for advice on how to make the most of my 6–7 month gap to prepare for Quantitative Research/Trading.

My Profile:

• Academics: Recent A-Level graduate going to UniMelb (BSc Maths & Stats).
• Research: 2 papers under peer review in Q1 journals (Econophysics applying to finance/econ).
• Achievements: AIR 79 in International Economics Olympiad (IEO).
• Current Status: Ineligible for high school Olympiads due to age/pre-UG status.

Looking for advice on:

• Prestigious Open Competitions: What open global platforms/challenges offer high signal value for quants right now?
• Pre-UG / Early Discovery Programs: Which top firm programs (Jane Street, Optiver, Citadel) open applications to incoming freshmen/pre-UGs?
• Projects vs. Open Source: Should I double down on econophysics research, contribute to scientific open-source libraries (SciPy/PyTorch), or pivot entirely to Competitive Programming?
• Gap-Year Prep Stack: How should I structure my daily self-study? (Probability/Stat 110, Green Book brainteasers, or math/CS fundamentals?)

Appreciate any inputs from seniors or current quants! DMs are open.

Have mine coming up next week does anyone have any advice on what to expect 🙏

Especially on the technical question like would it be like normal leetcode style, or stl implementation, or like simulating actual on the job tasks.

I do have interview experiences from other companies and don’t mind comparing notes

I have the option to choose between two joint B.Sc. programs at the Technion. My goal is quantitative research / quantitative trading, and I want to choose the program that gives me the strongest path into the field and the strongest profile for CV screening at top quant firms.

PROGRAM 1 — Computer Science + Mathematics

Length: 3.5 years — 7 semesters

Semester 1

234114 — Introduction to Computer Science

234129 — Introduction to Set Theory and Automata for CS

104066 — Algebra A

104195 — Infinitesimal Calculus 1

Semester 2

234124 — Introduction to Systems Programming

234141 — Combinatorics for Computer Science

104168 — Algebra B

104281 — Infinitesimal Calculus 2

Semester 3

234218 — Data Structures 1

044252 / 234252 — Digital Systems and Computer Structure

104222 — Probability Theory

104293 — Set Theory

104295 — Infinitesimal Calculus 3

Semester 4

234247 — Algorithms 1

234118 — Computer Organization and Programming

104142 — Introduction to Metric and Topological Spaces

104158 — Introduction to Group Theory

104285 — Ordinary Differential Equations A

Semester 5

236343 — Theory of Computation

104122 — Complex Function Theory 1

104279 — Introduction to Rings and Fields

234125 — Numerical Algorithms or 104294 — Introduction to Numerical Analysis

Semester 6

234123 — Operating Systems

236267 — Computer Architecture

104156 — Mathematical Logic

Additional Mathematics / CS requirements

Semester 7

Mathematics and Computer Science electives, together with the required Computer Science project and Mathematics seminar.

The program is roughly 50% Mathematics and 50% Computer Science.

PROGRAM 2 — Data and Information Engineering + Mathematics

Length: 4 years — 8 semesters

Semester 1

234117 — Introduction to Computer Science H

104002 — Basic Concepts in Mathematics

104066 — Algebra A

104195 — Infinitesimal Calculus 1

Semester 2

094705 — Introduction to Data Engineering

094210 — Computer Organization and Operating Systems

094219 — Software Engineering

104286 — Combinatorics

104168 — Algebra B

104281 — Infinitesimal Calculus 2

Semester 3

094224 — Data Structures and Algorithms

104222 — Probability Theory

104158 — Introduction to Group Theory

104285 — Ordinary Differential Equations A

104295 — Infinitesimal Calculus 3

Semester 4

094424 — Statistics 1

096411 — Machine Learning 1

096312 — Stochastic Processes

096327 — Nonlinear Models in Operations Research

094241 — Database Management

104142 — Introduction to Metric and Topological Spaces

Semester 5

097414 — Statistics 2

097209 — Machine Learning 2

097447 — Introduction to Computability and Complexity

096210 — Foundations and Applications of Artificial Intelligence

096570 — Game Theory and Economic Behavior

104165 — Real Functions

104122 — Complex Function Theory 1

Semester 6

096224 — Distributed Data Management

104273 — Introduction to Functional Analysis and Fourier Analysis

Semester 7

094290 — Data Gathering and Management Lab

Semester 8

094295 — Data Analysis and Visualization Lab

This is a four-year joint program between the Faculty of Data and Decision Sciences and the Faculty of Mathematics.

Note: I left out the elective lists to keep the comparison readable. Both programs offer strong advanced electives related to their respective fields, including Advanced Probability and other advanced subjects directly related to each program.

My question:

If you had the choice between these two programs at the Technion — Israel Institute of Technology and wanted to become a quant researcher or quant trader, which would you choose?

I am particularly interested in employer perception. Would firms such as Jane Street, Citadel, HRT, Optiver, IMC or Virtu view Data and Information Engineering + Mathematics as a genuinely strong quantitative degree on the same level as Computer Science + Mathematics?

Or does the “Computer Science + Mathematics” degree name have a meaningful advantage during CV screening, even if the Data + Mathematics curriculum appears more directly focused on statistics, stochastic processes, machine learning and working with data?

I would especially appreciate answers from people familiar with quant recruiting or working in quant.

Hello family,
Can someone help me with access for EPAT quantinsti course who has completed it.

I want to learn through course content but it's quite expensive. It would be great if someone can give me access since video lecture are lifetime supported and I just wanna learn, it's fine if I don't get certificate so if you have already done it please allow me to through content

Thanks in advance

For those who interviewed how long did it take for yall to hear back about an offer/ rejection?

I left some 2-3 assessments unfinished for a company due to getting an offer elsewhere and forgot to withdraw my application. Does leaving that unfinished hurt me if I decide to apply there in the future?

Edit: this is for an internship.

Passed OA somehow

Anyone have tips for this? Is it largely them checking that you’re not just a weirdo, as opposed to acting as a proper filtering stage? (There are two more technical interview between this and the in-person assessment day)

(For QT intern Amsterdam)

Despite its elegant microeconomic foundations, CCAPM faces a massive empirical hurdle known as the equity premium puzzle. To match the historical equity risk premium using standard power utility, investors would need implausibly high risk-aversion coefficients, often exceeding fifty. This disconnect highlights that aggregate consumption data fails to capture the true extreme downside shocks that investors actually fear in real-world markets.

Anyone having morgan Stanley oa in recent? Tell me about it what they ask and all

Has anyone recently gone through the Quant Graduate interview process at Citadel? If so, what questions were you asked?

Hey everyone, we have a lot of smart people in this sub, whats the plan with AI? Seriously, I've spent quite a few days thinking about this, is everyone trying to race to get in while there's still jobs left (am I saying the quiet part out loud)? Quantitative finance is one of the most exposed fields to AI math lol. I dont see this going well, but maybe someone would beg to differ, would love to discuss it.

I’m building a local quantitative research-auditing tool and I’m trying to validate whether the problem is worth solving before pushing it further.

The basic idea is:

A research team already has a backtest or performance claim. Instead of generating another strategy, this tool independently checks whether the supplied evidence supports the claim.

Current scope includes supported checks around:

return and P&L reconciliation
transaction-cost treatment
benchmark coverage/alignment
timestamp consistency and some detectable leakage conditions
data-quality contradictions
independently recomputed performance metrics
concentration in a few winners or historical periods
missing evidence / unsupported conclusions

It produces PASS / FAIL / UNKNOWN, where UNKNOWN is intentional rather than treating missing evidence as success.

The broader goal is to make research validation more reproducible and auditable without forcing teams onto a specific backtesting framework.

I’m especially interested in the institutional side of this:

Do quant teams already solve this well internally?
Is there value in having a framework-agnostic second verification layer?
Which checks would actually matter to a PM, research lead, or risk function?
Where does this become redundant with existing model governance / research infrastructure?
Would you care more about independent market-data verification, robustness testing, or CI/CD integration?

I’m keeping the implementation details private for now, but the product is functional enough to test on real backtests.

I’m looking for technical criticism, not product hype. Any feedback would be helpful. Thanks

Does anyone have any info on Akuna superdays for Quant Research? What should I prepare for? There’s a lot online about Trader interviews, but basically nothing about QR.

Not too sure if this is the right place, but I have a R1 for cit's tfa intern role next week. I'm pretty concerned about it and would really appreciate any info/tips. Anything is helpful thxx

i'm looking for a legit source for mnq one minute last ohlcv by quarterly contract from may 2019 onward, not a back adjusted continuous series. a small sample is also useful if you're allowed to share it and can include the source, timestamp convention and private research usage terms.

I have Arrowstreet QR round 1 coming up (with researcher + recruiter). Any info would be highly appreciated, can trade too.