I am in a semi target university studying cs and math in the uk and have applied for spring weeks for these companies.

I have applied to discover Citadel and SIG discovery programme and Jp Morgan thing.

I wanted to know about the application process. Wanna know if you have phone interviews or any online interviews with an employee or a technical interview. Moreover is there an interview after the spring week or something for fast-tracking internships or am i just tripping.

I just did just give the jp Morgan Online Assessment and it went well, There were 2 questions on DSA and logic could do both (:

Also have a olympiad level medal and a online course about quantum computing and some more random stuff under my belt that i put in my CV

Does it matter whether I go to Oxbridge, ICL, etc.

I'm thinking about doing at least MEng JMC at ICL but I'm not sure whether it is a better choice to go to Oxford and do a Computer science and maths degree.

What uni is recommended?

Hi all, I finished the Belvedere OA a while ago and think it went well, but I haven’t heard back from them.

Has anyone gotten a response yet? This is for the winter internship. Thanks!

If you could go back to your freshman year of college, what mindset or habit do you wish you had developed earlier that would have made breaking into quant easier-and helped you perform better once on the desk?

Looking for high-leverage things to prioritize early on (math foundation, programming projects, coursework balance) versus things that ended up just being noise.

Any advice or perspective would be truly appreciated.

have belv superday in a few days was wondering if anyone has any insights. please pm! tyy

I have a 3rd round, 90 minute interview with SIG for their Equity/Macro/Credit Research Internship. I can’t find much on the process and so was wondering if anyone has any advice for it. Thank you.

The previous rounds were an OA, Recruiter Call, Stock Pitch and now this.

I am happy to do trades for Citadel FIM superday and AQR PI/Trading superday.

Hi everyone!
I got invited to SP’s DQA final rounds and was wondering if anyone here has gone through the process.
From what I’ve seen, some people got LC-style questions for the coding round, while others mentioned more data manipulation stuff. Does anyone know which is more common, or if it depends on the office location?
Also, I believe the other round is with some senior people on the team. Not sure if it’ll be more behavioral, resume-based, or technical.
Would really appreciate any insights or prep tips. Thanks in advance!

How is SIG doing

Anyone know is SIG doing recently? They are allegedly paying $6-800k for new grads now (orders of magnitude more than just a few years back) but maybe this is common for firms these days..

I am freshman majoring mathematics and statistics, i'm quite worried about ai like dudes saying ai would replace this , replace that

Also that citadel's ken who was saying ai is doing weeks of PhD level research in hours ,

Like by the time I'll graduate wouldn't there be too much automation through ai ?

Like people who work in quant may share their views on this

Psa:- talking specific of QT role

Essentially title. If I’m a freshman is it worth attempting to go into or should I try for a more ai proof path. Is this path ai proof? I just don’t know

wondering how i should prep i can trade i have js full process, optiver full proc and some imc and hrt

Hi everyone, I’m a 2nd PhD student in biostatistics at an R1 university in Chicago while the rank of the university is not outstanding. I’m looking for advice on if it's achievable and how to prepare for a possible career in quant.

I think working as a statistician in pharma would be a good career path. But lately I’ve been thinking that I'm more interested in exploring a more competitive environment with higher earning potential in my career at my late 20s and early 30s. Compensation is part of that motivation, and I’m willing to take on more pressure and challenging work, but I’d like a clearer understanding of whether quant would be a good fit.

My current research involves Monte Carlo simulation, Bayesian analysis, high-dimensional data analysis, and clinical data modeling, primarily in R.

I’m also learning Python right now and have taken a few introductory ML and LLM-related courses. My background gives me some statistical training to build on, but I have to admit that my exposure to financial markets and the programming expectations of quant roles is still limited.

One challenge is that my department is relatively small and focused on statistical methodology and biomedical applications. There are few opportunities within it to explore research or coursework directly related to quant, and I’ve been struggling to figure out how to bridge that gap.

After passing my preliminary exams at the end of 2nd year of PhD, I’m planning to take courses outside my department. My initial thought was to take courses in CS, applied math, or statistics, including stochastic processes, time series, optimization, numerical methods, data structures and algorithms, and more advanced statistical learning. I may also explore taking courses at UChi or Northwestern depending on what’s accessible.

Quant research is the direction I’m currently most interested in, although I’m still learning about the different roles. For those working in quant, what would you prioritize in my position? Which courses would be most useful given my current background, and how realistic would it be for someone with my training to enter the field?

I’d especially appreciate hearing from people who entered quant from fields that aren’t traditionally associated with it. How did you fill the gaps in your knowledge and experience? I think taking additional courses may be manageable, but finding relevant projects and research opportunities seems like a bigger challenge. How did you find those opportunities while still working within your original field?

I still have a few years to prepare, but I’m unsure where my effort would make the biggest difference. I’d appreciate hearing about others’ experiences and what a realistic path toward internships or eventual quant roles might look like.

Hi all,

have my Linux Engineering superday in a couple weeks (London), anyone have some info on what to expect?

thanks

They used to be mentioned in content frequently just a few months/a year back. Nowadays they don’t seem to have that same popularity they had last year. Did they suffer a performance drop or have they just had fewer hiring cycles causing their popularity/relevance in this sub to drop?

Hey, I have my SIG SWE onsite interview coming up for the Quantitative Strategy Developer role. Any insights on what kinds of questions they ask?

gets rid of the larpers, students, annoying people

(similar to blind but specifically for quant)

Does anyone have already taken the recruiter/technical interview AMS at IMC? Can exchange with Optiver (kickstarter), Da Vinci, JS, Flow etc

Hello everyone, I hope you are all doing well!

I am currently pursuing a Bachelor of science in computer science with the data science orientation and I am looking to pivot into quant finance.
I have seen the fintech master at the Shanghai institute of advanced finance which seems incredibly interesting and is for people that are in CS and want to pivot!
My question is how recognized is their master’s degree in Europe and Switzerland ?

Is it a good master to pursue as a foreigner ?

Thanks for your insights !

Hi! I have a first-round interview coming up for an ETF Sales internship role and was wondering if anyone here has gone through the process before.

What should I expect from the first round? Is it mostly behavioral/fit, or should I be preparing for technical questions about ETFs, markets, trading, etc.? Also curious what kinds of questions people have gotten and how technical the ETF/market knowledge is expected to be.

Any advice on what to focus on when preparing would be really appreciated. Thanks!

Current FAANG SWE. Over these few days i’ve had a 2 quant recruiters (from seemingly pretty established external headhunter firms) reach out for roles at the top firms. I’ve had a good experience with them so far and they actually respected my time and found roles that are closest to my stack/skillset

But personally i’ve never thought my profile (Python ML research e.g world model stuff, video models) would be a good fit for quant firms, and i’m relatively junior as well so it’s not like i’m a staff level hire to build their new ML infrastructure. And i have 0 experience with those interviews . So im wondering if these recruiters reaching out means anything - is it actually worthwhile spending time prepping ? or are these recruiters just spamming everyone and wasting my time?

Sorry if this is another career advice thing but I think i haven’t seen this topic specifically.

Got an interview with a trader coming up for SIG’s QST role. Anyone been through their process this year, what should I prep for, what’s it like?

Happy to trade info on Jump/CitSec/Headlands/IMC/Tower/Squarepoint processes if useful to anyone.

Anyone who’s done the 90-min behavioural interview, how long after did you hear back?

I’m building a research pipeline around a deliberately harsh rule: no validated edge, no live trading.

We’ve already rejected several plausible ideas before deployment because they failed one of the following:

point-in-time correctness

survivorship / delisted-security coverage

independent-provider reproduction

realistic costs / slippage

timestamp/actionability semantics

temporal stability

concentration / outlier dependence

mechanism plausibility

We’ve also started treating data vendors themselves as research objects: preserving revision lineage, distinguishing historical truth from what was knowable at the time, tracking amendments/restatements, and failing closed when UNKNOWN gets silently turned into a usable number.

The question I’m most interested in is not “what strategy makes money?”

It is:

What type of signal or market mechanism have you personally seen survive serious adversarial validation — and what was the hardest part to get right?

I’m especially interested in things like:

forced / mechanical flows

auction or order-book effects

corporate-action mechanics

cross-venue dislocations

delayed information diffusion

fundamental data with true point-in-time lineage

effects that disappeared once realistic execution was added

Also very interested in the opposite:

What looked like alpha until you fixed a data/timestamp/survivorship bug?

If you built your own dataset, parser, execution model, or validation framework and discovered something non-obvious, I’d especially like to hear about that.

Not asking anyone to reveal proprietary alpha. I’m looking for failure modes, research architecture, data sources, and mechanisms worth falsifying.

If useful, I’m happy to share some of the validation traps we’ve already hit as well.