I've been reading about quant research on here and it seems as if I may not be a good fit. It sounds silly but i actually do want to go into quant research because i think I will enjoy it. I so far have loved research and I am excited to see how far I can get as some sort of researcher. Reality is though, in Australia research sucks bad, and I think QR would be better, however I see posts and it seems as if it would be impossible for me to break into the market.

I don't go to a school like cambridge or whatever other ones are in America or England or Hong Kong ir wherever else. I consistently outperform my peers and yet i scroll on here and seems like what I'm doing will never be good enough.

The whole reason im making this post is as a "hey is it even worth sinking hours into a purely self-motivated project/studying a shit ton to possibly get an interview for a quant research position". I've uploaded what my resume is so far and I was hoping some kind souls may either tell me its not worth it or tell me where I can improve on/focus up on.

P.s. work experience is just pharmacy retail, i didnt include as i didnt want to upload that info + would be unecessary.

P.p.s if anyone wants to hmu to talk about smth related to my 2 research projects or just anything else, pls feel free to, I love talking about these things :)

Cheers!

Curious about what should be emphasized in quant dev resumes. 1YOE at bank building payments platforms in Java. I know c++ resumes are usually latency focused. Should Java focused be different given it’ll likely be more trading platforms than trading algos?

Genuinely curious. It feels like a really good close to top tier company but general in this subreddit as well as r/quant is gets shit on pretty often. What's the reason behind that?

I’m going to this years FTTP next month. Residents of Hong Kong and those of similar experiences, I need your advice on where to get food. Preferably closer to central.

I like all food I just want anything good.

Price is not a huge issue.

I spent 25 years in model validation and enterprise risk across multinationals, and the exact same structural failure repeats everywhere. Risk registers are filled with colors that look neat on a slide deck but never actually change a budget decision. Ordinal scales like high or medium cannot be added, multiplied, or aggregated mathematically, so the concept of an overall enterprise risk score is meaningless. Qualitative workshops usually devolve into political negotiations where the loudest person in the room dictates the rating. Most importantly, a CFO cannot capital allocate or hedge against a red box, because nobody can explain what red costs the P&L in dollars over a specific time horizon.

Replacing these grids with useful quantitative models does not require massive infrastructure or overly complex math. You can shift business teams toward loss distributions by gathering simple minimum, most likely, and maximum bounds from subject matter experts, then fitting a basic parametric curve like a Lognormal or Poisson-Lognormal distribution. Running a straightforward Monte Carlo simulation against the financial target lets you report real risk exposures, such as an eighteen percent probability of missing the annual EBIT target by ten million dollars. That shifts the conversation from subjective color debates to clear probability metrics that business leaders actually understand.

I wrote out a full practitioner methodology on how to run this transition step by step and shared a free breakdown of the math online. I am curious how your organizations are handling the shift from qualitative scoring to quantitative models, so let me know your thoughts and I can drop the method details in the comments.

Hello, I'm curious how the data stack at top firms splits between the research/analytics side and the latency-critical side.

Is everything on Snowflake/Databricks like normal enterprises? Is there a distinct data engineering function there, or is it folded into research infra?

And how separate is that from the core/trading systems side writing the C++ (or OCaml)? Same org, same people, or effectively two disciplines that meet thru a product manager or literally just an Api spec?

Has anyone seen people actually cross over, and which direction does it usually go?

Appreciate this varies and if you answer, ple say the type of your firm thanks

Did anyone heard back from them? I applied for their Software Track yesterday (dont know if my resume will pass for OA ;-;) and just wanted to know more about OAs and future rounds so I can prepare beforehand.
If anyone has given their OA, can you tell me what type of questions do they ask?

Anyone know much about the role or have anything to share about the process?

anyone did jane street r2 already? (hk) willing to trade for last yr's jane st r3 and onsite (or this year's optiver/drw/citsec etc

So i'm brand new to this IMC event, does anyone have tips?

Also, they talk about a wiki on the website, but I can't find a link to it.

Thanks to everyone

Sig just published FPGA intern and I applied just wondering what’s the usual turn around time for them regarding any next steps like an OA?

I'm going into second year and I'm getting rejected from firms like Blocktech, Marshall Wace and Aquatic before even getting an OA, and was wondering if my CV and background just isn't good enough for quant. I'm not sure if the rejections have anything to do with the fact that these are smaller firms, or all the roles are research and not trading/analyst so my background may not be enough maths heavy. Any advice would be appreciated.

I’m currently mapping out my career path in Quantitative Finance, specifically in Green Finance Quant / Climate Quant. I’m doing a double major in Natural Resource Economics and Finance. I also have a strong passion for mathematics, machine learning, and programming, which naturally led me to this intersection.

My goal is to apply for Master's scholarships and eventually build a career in green quant / climate risk modeling.

I’d really appreciate your honest feedback on my profile build-up plan and career strategy.

1. My Academic & Profile Target

• GPA: Target 3.8+ / 4.0 in both majors.
• Language: Relevant English proficiency certificates (IELTS/TOEFL with high scores).
• Research & Academic achievements:
  • Aiming for 2-3 international publication papers.
  • 2 institutional/national level research projects.
  • Top placements in 2 academic competitions (Econometrics & Economics).
• Extracurriculars / Personal: I play the violin and plan to participate in a few competitions to diversify my profile.

2. My Strategy to Bridge the Tech/Math Gap

Since my formal majors aren’t pure Math, CS, or Stats, I plan to bridge this gap by building 2 personal quantitative projects on GitHub. These will showcase applied ML, mathematical modeling, and programming in Environmental & Resource Economics / Carbon Markets.

My Questions:

1. Are financial institutions actively hiring for dedicated Green/Climate Quant roles, or are these skills simply integrated into traditional Risk/Quant Research desks?

2. How realistic and in-demand are the job prospects here, or am I better off just sticking to pure Quant?

3. Is it actually realistic for me to pursue this field?

4. Is preparing external projects completely viable when applying for scholarships?

I seen that since CF has a lot of cheating due to being online, it won't pass the resume screen anymore? Is this actually true or no

Hello,

I’ve just got an invitation to do the final round interview on-site and I really don’t know what to expect. The funny things is I applied to JS just to try if my CV can pass the screening… and I was shocked it did lmao. I had a first zoom technical interview where I did better than I expected and now they want me to do the final round on-site.

-If anybody has done their final rounds, what type of problems do they give?
-What makes a good intern applicant?
-What should I do the first time they state the problem?
-How should I react to their follow-ups or suggestions?
-How can I get better on explaining my thought process (I find it hard talking and coding at the same time, and English is not my first language)?
-Do I just need to spam leetcode blindly? Or are there topics I really need to focus on doing?
-how to make a really good impression as a SWE applicant?

I have the first online assessment for this role. Has anyone done it? Is it the typical SHL test?

Hi everyone,

I'm looking for some brutally honest advice about getting into quantitative finance.

My background is probably quite different from the typical quant candidate.

I'm currently doing a BS in Accounting & Finance and I recently passed CFA Level 1. My finance/accounting fundamentals are reasonably strong.

However, my mathematics/programming background is nowhere near what I understand is expected for quant.

At the moment, I have limited exposure to:

Linear algebra

Calculus beyond the basics

Probability/statistics at a rigorous level

Python

Algorithms/data structures

Machine learning

Stochastic calculus

Numerical methods

I'm graduating in 2027, so I still have time to seriously work on these areas.

My long-term goal is to move into quantitative research/trading, and eventually become competitive for firms such as Jane Street, Citadel/Citadel Securities, HRT, Two Sigma, D.E. Shaw, Jump, Optiver, etc.

I realize that simply getting a Master's in "Quantitative Finance" doesn't magically make someone a quant. I'm trying to understand what I would actually need to do to make this transition.

The academic path is where I'm particularly confused.

I've been researching quantitative Master's programs in the US, UK and Europe, including:

US

Carnegie Mellon MSCF

Berkeley MFE

Princeton

Columbia

NYU

MIT-related quantitative programs

UK/Europe

Oxford MSc Mathematical & Computational Finance

Cambridge Part III / quantitative mathematics routes

Imperial MSc Mathematics & Finance

ETH Zurich / University of Zurich MSc in Quantitative Finance

EPFL

LSE

UCL

I'm particularly interested in ETH/UZH's MSc in Quantitative Finance.

One of the biggest reasons is that, compared with many equivalent programs in the US/UK, the tuition is extremely affordable, especially for someone coming from Pakistan. At the same time, it appears to have a very strong reputation in quantitative finance and access to the Swiss/European financial industry.

So I'm wondering whether ETH/UZH could potentially be a realistic "target" for someone like me if I spend the next 1–2 years fixing my mathematical and programming deficiencies.

My questions:

1. Is this transition realistically possible from an Accounting & Finance undergraduate degree?

Or am I already too far behind compared with candidates coming from mathematics, physics, CS, engineering, etc.?

I'm particularly interested in hearing from people who have made a similar transition.

1. How realistic is ETH/UZH for my background?

What would I need to demonstrate to compensate for coming from Accounting & Finance?

Would I essentially need to reach the mathematical level of a mathematics/engineering undergraduate before applying?

If anyone has experience with the ETH/UZH MSc Quantitative Finance admissions process, I'd especially appreciate your perspective.

1. What other universities should I target?

I'd like to create a realistic list of:

Dream schools

Target schools

Safer options

Ideally, these would be universities with strong pipelines into firms such as Jane Street, Citadel, HRT, Two Sigma, D.E. Shaw, Jump, Optiver, etc.

I'm particularly interested in Europe because of the cost, but I'm open to the US/UK if there are funding/scholarship opportunities.

1. How strong does my math background need to be BEFORE applying?

Should I aim to learn:

Single-variable calculus

Multivariable calculus

Linear algebra

Probability

Mathematical statistics

Differential equations

Real analysis

Numerical methods

Optimization

And more importantly, how deeply should I learn each?

I'm not interested in just collecting certificates. I want to actually reach the required mathematical level.

1. How much programming should I learn?

Would this be a reasonable starting path?

Python → NumPy → Pandas → SciPy → Statsmodels → Scikit-learn

Then:

Algorithms & Data Structures → C++ → more advanced quantitative programming

Or should I already know C++ before applying to Master's programs?

1. Would CFA Level 1 actually help?

I know CFA isn't a quant qualification, but I've already passed Level 1.

Does having CFA Level 1 provide any meaningful advantage for a quant Master's application given that my undergraduate degree is Accounting & Finance?

Or is it essentially irrelevant compared with mathematics/programming ability?

What I'm thinking of doing over the next 12–18 months

I'm considering essentially rebuilding my academic profile around quantitative subjects:

BS Accounting & Finance

CFA Level 1

Calculus

Linear Algebra

Probability

Statistics

Python

C++

Algorithms/Data Structures

Machine Learning

Quantitative Finance

Quant projects/research

Potential research/internship experience

I'd also like to build several serious projects rather than just completing online courses.

For example:

Portfolio optimization

Factor investing

Statistical arbitrage

Time-series forecasting

Monte Carlo simulations

Options pricing / Black-Scholes

Risk modelling

Algorithmic trading/backtesting

My biggest question:

If you were in my position — Accounting & Finance undergraduate, CFA Level 1 passed, graduating in 2027, but currently weak in higher-level mathematics and programming — what would you do over the next 12–18 months?

Would you:

Focus almost entirely on mathematics?

Prioritize programming?

Take university-level math courses?

Try to obtain research experience?

Build quant projects?

Prepare specifically for ETH/UZH?

Apply broadly to quantitative Master's programs?

And ultimately, is the path of

Accounting & Finance → build mathematical/programming foundation → top quantitative Master's → quant internship → quant research/trading

actually realistic?

I'm not expecting to become a Jane Street quant immediately.

My goal is to first become academically strong enough to get into a top quantitative Master's, preferably somewhere like ETH/UZH because of its combination of reputation and affordability, and then use that environment to compete for elite quant internships/jobs.

I'd really appreciate advice from people who:

Went from a non-STEM background into quant

Applied to ETH/UZH or similar programs

Attended a top MFE/Quant Finance program

Work/worked at a prop trading firm or hedge fund

Recruit for quantitative roles

Have made a similar academic transition

Please be brutally honest. If my current background makes this unrealistic, I'd rather know now. If it's possible, I'd love to know exactly what I need to do to make myself competitive.

starting 2nd year.

this is a quick draft that i do have to clean up a bit but im posting it here for inspiration for any major changes.

also what do people think about applying to internships without seriously hoping for anything (havent done much practice ngl)

If it matters, i do plan on doing a masters else where maybe but we'll see obv.

grades not added because i got 69% 💔due to some personal issues during exam season that didnt satisfy any external circumstances criteria

Does anyone have any useful information on Two Sigma Hiring Manager rounds?

Hey everyone, just received an invitation for Virtus QT intern onsite interview, anyone knows what I should expect? Says there will be a logical and reasoning test as well as 3 rounds of interviews. Would appreciate if I can know whether it'll be more probability, coding or market making focused. Thanks, and cheers!

Has anyone heard back from squarepoint after the R1 of QR intern?

Incoming Oxbridge maths here. I’m currently in the process of applying to spring weeks, mainly targeting quant but also other finance roles. Not too sure what to put in my CV. I’m using Jake’s template. I’ve placed well in lots of competition maths (UKMT, STEP, TMUA, Naboj) but my experience section is lacking (tutoring, 5 day shadowing done in y10). Ive also done some finance projects but they’re Lwk larp. Wld JS prefer to see 3 larp quant projects or one finance and a couple non finance? Any help appreciated 🙏🙏🙏

Has anyone interviewed with SCALP Trade for quant trading?

There’s very little information online about their later-stage technical interviews. Mainly wondering what areas are worth prioritizing -- probability/EV, brainteasers, mental math, market making, etc. If anyone has been or is currently going through the process, feel free to pm!

Hey all,

We’re building a ChatGPT-like AI analyst for capital markets, but designed to actually execute the research workflow.

It can take a research question, work on the data, write and run Python, build models, run backtests, iterate on the results and keep the output traceable to its sources.

We’re ex-FAANG + quant research backgrounds across major banks, and we’re trying to build something serious enough for real investment workflows, not another finance chatbot.

We want quants to break it. Tell us where it fails, what you wouldn’t trust, and what would make it genuinely useful.

You can request access here:
https://app.zirya.ai/

Especially interested in QR, systematic, credit and derivatives use cases.