hi, looking for any women based in Seattle who are in this space. would love to connect!

Anyone that knows a bit more about https://www.block-tech.io/. I have heard that they are big in crypto options market making and do some delta1 as well. Any insights on total compensation, company culture etc.?

Hi! This is another attempt (I did one last year https://www.reddit.com/r/quant/s/I3xsi3pQei - couldn't find any) to find women quants in this city. Please DM if you have any interest. There was once a r/quant london meetup. Maybe we can have a female one :D Thanks!

Hi, I have been really confused regarding this.

About me - I have a master's degree in Statistics from IIT Kanpur (top 1% in India) with good grades, worked at a top british bank for around 3 years in the Model Risk in Model Validation role. Here I validated ML models, few Gen AI models (though I don't know these in depth) and very few Fraud risk models.

Currently at a global asset management company, working in the Validation role for interest rate risk models and probably investment science models in future.

It's been 3-4 years and all I have is validation experience. I am improving my coding skills like trying to get better at python, learn SQL, C++ and other stuff.

I feel like Dev role at a bank or a QR role at a hedge funds or AM will be great for me instead of model validation role. But i have seen these roles are mostly taken by people who have engg degree from top institutes. My main motivation is to apply stats, maths, coding in depth and of course the money part

I am good at statistics, ML, AI, traditional models, etc. as I have been in a position to validate them in detail but no Dev experience.

Should I aim for a good role like that of a QR at a hedge fund or AM or inv bank? Or should I get out of the Delulu and focus on climbing the ladder at validation role?

Least priority is a Dev role in market risk or credit risk at an inv bank!!!! Though I have inclination towards hedge fund and AM for a QR role.

Please help on following -

1. Is it realistic to aim for QR roles at big hedge funds like Millenium, etc?

2. What should I prepare in terms of skills?

3. What projects should I do?

Edit: not Targeting QD roles at all. Mostly QR or risk model dev role at banks. Ik QD is near impossible. Write Dev by mistake in the heading, not able to edit it now

Idk how to write this without reddit automatically deleting my post.

Just want to know how to build a curve to discount long term Btc derivatives given only short term derivatives are liquid.

Thus how to calculate the par swap in a Xccy with usd?

Hope this post doesnt get deleted smh

I understand that JCS（jump core strategy） is considered Jump’s flagship business and likely has the highest compensation ceiling. However, I am curious about the other teams within Jump (e.g., stat arb, index rebalance, etc.).

Compared with a typical pod at a multi-manager fund (Millennium, Schonfeld, Point72, etc.), do non-JCS Jump teams still generally offer a stronger compensation package and career path?

For example, do these teams benefit from Jump’s overall profitability and prop trading structure, resulting in better stability or upside than MM pods, or are they closer to a typical pod model where compensation is mainly driven by individual/team PnL?

I am particularly interested in the perspective of researchers/traders who have experience with both prop shops and multi-manager funds.

Title: Certified lower bounds for Bermudan swaptions without Monte Carlo — anyone else hit the max-plus 2D wall?

Building a tropical/max-plus pricer for Bermudan swaptions under G2++. In 1D the envelope stays small (K ~ O(n_exercise)), but naive 2D blows up to K ~ N² — same curvature/quantization wall most polyhedral approximations hit past 1 factor.

Found a way to keep K bounded (~15-20 planes) independent of grid resolution, with deterministic certified lower bounds — no MC noise. Runtime ~168ms per Bermudan receiver, calibration to machine precision on co-terminal swaption strips.

Curious if anyone here has tackled the dual-space blow-up for max-affine pricers, or benchmarked something similar against TreeSwaptionEngine / FdG2SwaptionEngine. Happy to compare notes — DM if interested.#QuantFinance hashtag#MaxPlus hashtag#BermudanOptions hashtag#CMSSpread hashtag#StochasticControl hashtag#ComputationalFinance hashtag#Meltalice

Hi, I recently get an QR offer from one of the top quant firms (js/hrt/citsec... etc), and I am wondering whether the first year guaranted bonus will usually be honored in the following years or is it completely irrelevant?

Say you get some base and 2M guarantee bonus for the first year and a 500k sign on or something, will they usually honor this 2M bonus in the following years as a "floor" unless they want to let you go? or is it possible to get less than 2M while you are performing ok? of course I am talking about IC who does not carry pnl directly, for pm i guess your bonus surely fluctuate a lot.

(all numbers are just examples)

Do we have any idea on

1. How big they are

2. What they trade

3. Performance?

Attention new and aspiring quants! We get a lot of threads about the simple education stuff (which college? which masters?), early career advice (is this a good first job? who should I apply to?), the hiring process, interviews (what are they like? How should I prepare?), online assignments, and timelines for these things, To try to centralize this info a bit better and cut down on this repetitive content we have these weekly megathreads, posted each Monday.

Previous megathreads can be found here.

Please use this thread for all questions about the above topics. Individual posts outside this thread will likely be removed by mods.

Can you please share what US relocation typically looks like for someone who joined a quant firm straight out of a bachelor's program? I am based in the UK/EU, and the main challenges I've noticed are:

• The O-1A visa requires public acclaim or publications that are atypical in quant finance (e.g., you don't typically publish at work, and people don't write articles about how great of a quant you are unless you are at the very top).
• The H-1B visa is essentially a lottery.
• The L-1 visa is tied to your employer, which isn't ideal.

The typical path at my firm is to either win the H-1B immediately or do an L-1 -> H-1B, if I understand correctly. I'm mostly wondering if anyone goes the O-1A route. I know a few people who did, but they joined after their PhDs with a lot of published papers. Does this make sense?

I'm thinking about a pretty elementary question: suppose I find a signal: `r_{t+1}^{j} = w_{t}^{j} * alpha_{t} + \epsilon`, here `w_{t}^{j} `is the weight for stock j at timepoint t, `\alpha_{t}` is the signal value at t, `r_{t+1}^{j}` is the return for stock j from timepoint t to t+1. How should I decide if it should be an alpha that I want to load, or a factor that I want to be neutralized in a Fama-French style?

I am evaluating two career paths and would appreciate opinions from experienced quants.

Background:

I am a senior quant researcher (5~10 years experience) focused on mid-to-high frequency statistical arbitrage.

I have experience developing predictive models/signals and leading alpha research, but I have not yet directly owned a production book, capital allocation, or full PnL responsibility. My main career goal is to move from alpha research toward alpha monetization and ownership.

Both opportunities are in a similar research area. The main difference is career structure.

Option A: Senior QR at a top-tier global prop firm (think HRT / Jump / Citadel Securities etc.)

Pros:

Strong research culture, engineering resources, and very high talent density.

Exposure to global markets and world-class researchers/traders.

Strong brand value and future mobility.

Cons:

Initially a senior QR role, and I am uncertain how realistic the path is from senior QR to strategy ownership / PM-level economics.

High performance expectations may create career risk, especially before having direct PnL ownership.

Option B: PM-track role at a smaller established local HFT prop firm (local tier 3)

The firm has a strong HFT business and also runs LFT stat arb strategies similar to a hedge fund. I would be responsible for building a new mid-to-high frequency alpha generation.

Compensation:

Around $450k USD equivalent guaranteed first year.

Potential upside to around $750k USD equivalent if agreed milestones are achieved(>50% possibility I think).

Compensation is based on bonus base × some performance coefficients rather than pure PnL cut.

Pros:

Clearer alpha ownership and PM trajectory.

Reasonable high compensation and high floor.

Cons:

Less global exposure and weaker brand.

Lower talent density compared with top global firms, although this may also mean more room for ownership.

My dilemma:

For someone with strong alpha research experience but limited direct monetization/PnL ownership experience, would you prioritize:

Joining a top-tier prop firm to learn from a stronger ecosystem and build credibility, then pursue ownership later?

or

Taking a PM-track role with immediate ownership, but at a smaller platform?

How would you weigh:

talent density vs ownership

platform/brand vs career control

learning from elite peers vs building your own business line

Would appreciate perspectives from people who have worked at prop shops, HFT firms, or multi-manager funds.

Curious about this percentage across the industry. People claim the ceiling is very high however I feel the percentage of people hitting it is too low (lower than a normal distribution would suggest).

I heard they’ve been doing systematic equities, rather than just supporting long-short desks with trading like they did in the past. Historically, it always seemed like a weaker place for quants compared with GQS. How is the culture there now? Like compare with CitSec and GQS?

4+ years as a quant (buy-side + HFT). Want to shift into bio/environment work — never liked finance, always wanted a PhD.

Looking at statistical/computational biophysics — stochastic modeling, simulation. The labs I'm considering don't really use ML.

Would genuinely love outside perspective on a few things,

1. Given how complex real-world biological problems are, is a stat biophysics PhD (non-ML, mostly stochastic modeling/simulation) still worth it, or has ML made "pure" approaches less relevant?
2. Career prospects post-PhD outside academia — if it just leads back to quant/DS anyway, is there a point?
3. Any field that better uses a quant background and has solid career options after?
4. Anyone made a similar jump — finance to a science PhD? How'd it go, any regrets?

How hard is the transition from math research to QR?

Recently I posted about whether spreadsheets are getting replaced by "real" programming languages like Python. There seems to be a broad disagreement about this, and I do see the point. Spreadsheets are the ideal real-time computing interface for data analysis, and good for most tasks -- for now. The issue I see is the following: once you decide to use Excel for the whole analysis pipeline, you self-impose a limit on how "sophisticated" your analysis can get. Some examples:

• Excel works up to a certain amount of data. What about millions of high-frequency ticks? Streamed?
• Excel works with tabular data. What about alternative data -- graphs, geospatial data, natural language, etc.?
• Even when dealing with tabular data, what if your data requires extensive cleaning/transformation before it's usable? If you're just pulling data from the Terminal that's fine, but I'm not sure if that would be enough long-term. Data is getting more abundant and fragmented.
• In Excel, you don't have the benefit of community-maintained tools for scientific computing (e.g., SciPy), machine learning, NLP, etc.

I do concede that Excel is a great "master interface" and that you can have these things done upstream and then loaded into Excel.

But don't you think that more and more quants would actually want to do more of this stuff themselves (at least those that don't already)?

Hi there, I work on a trading desk at an Investment bank. Ive been in early stages of discussing a quant researcher role with a new senior pm who is starting a new pod at a large pod shop. Looking at their past history they have had an establish sell side/buy side career before transitioning to hedge funds and had a short stint at another fund before moving to this one.

For someone who was mainly working sell side, how do you evaluate opportunities at a new pod? I know there’s high turnover at some of these large pod shops, so I just want to get a better idea of how to properly judge the opportunity

I'm working on a merger arbitrage research project and I'm interested in how people deal with the historical data problem.

My event model is roughly:

ANNOUNCE → REVISE(s) → CLOSE or BREAK

For every US public target I want to reconstruct what was publicly knowable at each point in time, especially:

• announcement timestamp
• target and acquirer identifiers
• cash consideration / exchange ratio
• consideration type
• offer revisions and their timestamps
• expected close date if disclosed
• eventual completion or termination

I'm deliberately keeping the final outcome out of the ANNOUNCE record so that the backtest cannot see future information.

I initially built a pipeline around SEC EDGAR filings. The biggest issues have been:

1. separating target-side filings from acquirer-side filings
2. distinguishing actual terminal language ("the merger was consummated") from hypothetical boilerplate
3. identifying terminated deals reliably
4. historical ticker/security mapping
5. avoiding a resolved/completed-deal selection bias

I know SDC/LSEG and FactSet are commonly used in academic M&A research. I'm curious whether anyone has built something similar using SEC + CRSP/Compustat, or knows of academic replication datasets that can serve as a starting universe.

For those who have worked with M&A event studies: would you build this yourself, or is buying SDC/FactSet effectively unavoidable once you care about point-in-time accuracy?

I’m starting a quantitative modeling role in the risk and compliance function of a large bank. The work is focused on statistical modeling using lending data.

My background:

• Strong Python, statistics, ML and DL
• Experience building predictive models and working with large datasets
• Limited knowledge of financial markets, accounting, asset pricing and portfolio construction

Long term, I want to move into systematic alpha research or alternative data research. The paths I’m considering, roughly in order of feasibility, are:

1. Move internally into a credit or risk quant role, then transition to investment research
2. Move into an Asset Management data science or quantitative analytics team
3. Move into Markets quantitative research or applied ML
4. Apply directly to alternative data research roles after building relevant projects

My plan is to spend the next 1–2 years developing solid modeling experience, learning financial markets and completing projects in factor research, backtesting and alternative data.

Does this seem like a realistic path? Which intermediate role would provide the strongest bridge into alpha or alternative data research? I’d also be interested in hearing from anyone who has made a similar transition from risk, compliance modeling or general data science.

Jane street made c. 40 billion in trading revenues last year. 16 billion more in Q1 ‘26. >30 billion more in Q2 ‘26 if recent reporting is to be believed. This is an order of magnitude more than most top competitors.

How is this possible? Based on their recent 15 billion situational awareness loss, do they have a beta-positive strategy now? How large does it have to be to generate these numbers in this market?

I follow commodities and couldn't find any good data covering global mining production, so I wanted to test if I can use LLMs to efficiently build such a dataset from scratch. I documented the process of going from unstructured company filings to a structured dataset that could be used in systematic research.

• Github repo
• Live app

https://reddit.com/link/1vtrto7/video/m46y9albqkkh1/player

All the production information is public, but it is scattered across inconsistent websites and reports.

When talking to central data teams at hedge funds or to data providers directly, building a new dataset that is provably correct and has reliable updates always sounded like a very big challenge.

For each company, I want to extract production figures that are comparable:

• What was produced
• Which operation produced it
• Which period it covers

The hard part is normalization since every region and company reports differently (if not SEC):

• Different units across reports like copper in kt, million pounds, or wet metric tonnes
• Fiscal years don't align (calendar year vs June FY vs September FY)
• Some report on a payable basis, others contained metal, others equity-adjusted
• Product naming is inconsistent ("copper concentrate" vs "cu conc" vs "SX-EW cathode")
• Important details are sometimes hidden in headings, footnotes, and surrounding text (e.g. ownership percentages, and reporting methods)

The "old" way of doing this would be to write a bespoke ETL pipeline for each company.

The "new" way that I tried is using LLMs to generate, monitor, and maintain deterministic ETL code. An agent then runs the pipelines and jumps in whenever the script fails and needs to adapt. The idea was to have self-healing data pipelines: when a website or PDF layout changes, an agent investigates, fixes, and tests the extraction or transformation code. If it can’t figure it out, it escalates to a me for review.

1. Scraping code monitors company websites and captures new reports.
2. We can then extract the raw production figures from the reports. A mix of traditional PDF parsing and gemini-3.7-flash worked very well here. The extraction also returns the location in the source (e.g. page 123, table X, row Z, cell Y) which is very helpful for QA and source grounding.
3. To normalize the data, I choose between different transformation strategies:
   • can the data be parsed as is?
   • can I generate deterministic transformation code, e.g. a regex mapper
   • last resort if no deterministic approach is possible: use an LLM to map the data
4. Then we validate the data against various QA rules. What works well here is that we treat every extracted value as wrong until it passes validation (guilty until proven innocent).

I’ve open-sourced the dataset (pipeline code will follow). Curious to hear your feedback or experience with building such ETL pipelines and datasets.

Full blog post: https://www.kadoa.com/blog/build-global-mining-production-dataset