Balyasny Asset Management is a multi-strategy hedge fund founded in 2001 in Chicago by Dmitry Balyasny, Scott Schroeder and Taylor O'Malley. It invests through independent portfolio manager teams in equities, macro and fixed income, commodities, credit and systematic strategies, manages around $37bn, and has about 2,000 to 2,800 people across more than 20 offices including New York, London, Hong Kong, Singapore, Dubai and Abu Dhabi.
The pod structure is the thing to understand before you apply. Quant interns and researchers are hired into a named team, for example the Multi-Asset Arbitrage teams in Greenwich and Boston, and the people who interview you are the people you would work for. The firm says it sees up to 40,000 applications a year, that interviews are with the campus team and the team you might join, and that around half of its interns return full time.
The only online test reported for quant roles is a Python-heavy HackerRank, described as a programming problem plus a data science section, with 2 hours reported once. The interviews that follow are about statistics and regression, not brainteasers. Several candidates were asked for the closed forms of OLS, ridge and lasso, and one was rejected on the spot for not knowing the assumptions of linear regression.
- 1First call. An HR or campus screen, or the hiring portfolio manager. The order against the test varies.
- 2HackerRank. Python programming plus a data science section.
- 3Technical round. 45 minutes on Zoom with a team member: your projects, then regression and statistics.
- 4Superday. About 2.5 hours of back-to-back interviews with the team. A take-home with a video presentation has been reported for full-time roles.
Because teams run their own hiring, the order is not fixed. The order varies: the HackerRank can come before any call, as the first round, or after a first meeting with the project lead. Candidates describe the quant researcher process taking about 4 to 6 weeks.
The 2027 programmes
Every summer internship is 10 weeks. The firm's FAQ says it sponsors work authorisation for international intern candidates. Applications go through BAM's own portal, one application per role, and the 2027 postings opened from mid August 2026, with the Hong Kong roles already closed by mid September and London from late September.
| Programme | Location | Who |
|---|---|---|
| Quantitative Researcher, Multi-Asset Arbitrage (summer) | Greenwich, Boston | Bachelor's or master's students graduating between winter 2027 and summer 2028, placed in a Multi-Asset Arbitrage team |
| Quantitative Researcher, Systematic Strategies (summer) | New York | PhD students |
| Quantitative Researcher, Quantitative Strategies (summer) | London | Opened 29 September 2026 |
| Quantitative Researcher, Risk (summer) | London | Master's students graduating winter 2027 to summer 2028, with Python, probability and statistics |
| Quantitative Analyst, Macro and Commodities (summer and off-cycle) | London | 2027 cycle |
| Quantitative Developer, Systematic Investment Teams (summer) | Chicago | CS or STEM, C++ or Python, data pipelines |
There is no named quant researcher graduate programme. Full-time researchers are hired directly into teams. Rotational programmes exist for macro, commodities and technology.
Stage 1: First call
The first conversation is one of 3 things, and candidates report all 3.
- HR or campus screen. Background and motivation: why BAM, why hedge funds, why leave your current role.
- The hiring portfolio manager. Reported questions: the projects on your resume, how you find an alpha signal in your current work, what you would contribute to the team, then linear regression and p-values.
- A 30-minute team screen. Resume questions with deep follow-ups based on real situations in your work. For senior hires: what alpha were you working on, and what research infrastructure would you need.
Prepare a 2-minute account of your best research project that covers how you found the signal, how you validated it and how you would combine it with others. Decide in advance what you will not disclose about a current employer's strategies. Be careful how much detail you give about your own strategies.
Stage 2: HackerRank
| Item | What candidates report |
|---|---|
| Platform | HackerRank, consistently over several years |
| Content | A programming problem plus a data science problem An older Chicago quant risk report describes data analysis and some maths problems |
| Example | Return the n largest drawdowns of a return series. The data science part asked ad hoc, non-theory questions about the research process |
| Length | About 2 hours, in the few reports that give a length Not confirmed for interns |
| Difficulty | Candidates disagree. One found the programming questions hard, another called it a simple Python test |
| Pass bar | Expect to need an answer to every question within the 2 hours to continue. No quant report gives a bar |
For quant developers no first-hand report exists. Adjacent technology roles in 2026 describe SQL plus LeetCode-style problems and 2 LeetCode mediums, so expect that shape. No game-based, psychometric or video assessment has been reported for quant roles. We found no candidate report of a 90 to 120 minute test of 3 to 5 LeetCode hards.
Question count, proctoring, the invitation wording, the sitting window and retakes are not reported. If you have sat the test, tell us what you saw.
How to prepare
- Return series in Python. Drawdowns, rolling statistics and ranking without look-ahead, written from scratch under a clock. The time series alignment and streaming statistics lessons cover the patterns.
- Research process questions. Be able to write 3 sentences on how you would clean a dataset, handle outliers and validate a signal.
- For developers. SQL joins and aggregates, and 2 LeetCode mediums in 60 minutes from the coding practice catalog.
Stage 3: Technical round
45 minutes on Zoom with a member of the team. It opens with your resume and moves into statistics. The questions focus on statistics concepts rather than brainteasers, though a phone round of probability brainteasers is possible. Prepare both.
What they cover
- Regression. The closed forms of OLS, ridge and lasso. The assumptions of linear regression and what happens when they fail. p-values. A typical opener: "What is ridge regression?"
- Probability. The expected value of X given X is greater than Y, for independent standard normals. Finance, Python and probability questions together in one round.
- Alpha research. Combining alphas and computing their statistics. How you would analyse data whose predictors have outliers.
- Machine learning and time series. Candidates report only ridge and lasso. Broader machine learning and validation on autocorrelated series may also come up, so prepare them, but treat them as unconfirmed.
Balyasny's head of quant research, Giuseppe Paleologo, told eFinancialCareers in 2026 that being an outstanding coder is not much of a competitive advantage any more, and that the premium is on knowing which technique a problem needs, on communication, and on knowing linear algebra, stochastic calculus and probability in detail.
How to prepare
Derive OLS and state its assumptions and diagnostics without notes, then the ridge and lasso forms and why lasso has no closed one. The conditional expectation lesson covers the normal-variable question, and p-values covers the inference. For the validation claims, the backtesting interview questions are the right rehearsal.
Stage 4: Take-home and superday
Intern superdays run about 2.5 hours, with a statistics focus. Full-time researcher processes can add a take-home exam after 2 phone rounds, then a video presentation of it. They found the take-home unusual but not difficult: handle the data outliers and do not overthink it. About a fifth of quant researcher candidates report a presentation, which fits.
Quant developer processes include 2 algorithmic rounds, 1 system design round, 1 maths and statistics round and several behavioural rounds with recruiters and the head of HR. Data engineers in the research division sit a 1.5-hour Python coding interview, then about 4 hours onsite of Python, system design and behavioural. A reported design question is a key-value store.
How to prepare
Treat the take-home as a research exercise with a deadline: clean the data, handle the outliers explicitly, state your validation and keep the model simple. The validation course and its project defence lesson are the right preparation for the presentation that follows. Developers should work the Quant Dev 50 and the system design section of the systems course.
Key tips for success
- Learn the assumptions of linear regression cold. A weak answer can end the process on the spot.
- Write Python daily before the test, even if your research is in R. Its weight in the process surprises candidates.
- Prepare a defence of your own research, including what you will and will not say about a current employer's work.
- Learn the team. You are being hired by a specific portfolio manager, so know what the team trades and ask about it.
- Expect variation. A process run by a portfolio manager may skip the HR screen or move the test, and that is normal here.
Closing remarks
Balyasny's quant interview is the pod model applied to hiring. A short firm-wide screen on Python and data handling, then a team that wants to know whether you understand regression properly, can find and validate a signal, and can explain it. Candidates who do well bring one piece of research they can defend for 10 minutes, and know the statistics behind it in detail.
Our Millennium guide covers the same team-led shape at another multi-strategy fund, and our Brevan Howard guide a macro version where a firm-wide screen comes first. Work through everything we have for Balyasny, and read our quant researcher interview process guide for how the stages compare across firm types.