practice a little every day, paws crossed ♡
Quantitative Researcher Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
- 9403
- Questions
- 546
- Companies
- 11
- Topics
- 8
- Languages
Showing 1–30 of 59 results
- DRWHardQuantitative ResearcherExpected Dart Score for a Bivariate Normal Throw with Ring Scoring
Calculate the expected score of a dart throw modeled by a zero-mean bivariate normal distribution with circular scoring rings, calibrated using a known hit probability inside the inner ring.
Statistics & MathOnsiteProbabilityBivariate NormalExpected Value - DRWHardQuantitative ResearcherExpected Rounds Until a Shopping-Cart Line Empties Under Random Transfers
Tests modeling discrete-time Markov chains, formulating hitting-time difference equations, and solving symmetric random walk absorption problems analytically.
Statistics & MathOnsiteMarkov ChainsProbabilityRandom Walk - DRWHardQuantitative ResearcherExpected Tosses Until HHT Given HHT Appears Before HTH
Calculate the probability that the coin sequence HHT appears before HTH in repeated fair tosses, and find the conditional expected number of tosses until termination given that HHT appears first.
Statistics & MathOnsiteprobabilitymarkov-chainsconditional-expectation - DRWHardQuantitative ResearcherExpected Winnings in a Random-Walk Money Collection Game on 45 Stones
Calculates a player's expected winnings in a random-walk money collection game on an odd-sized circle of stones using Gambler's Ruin and parity arguments.
Statistics & MathOnsiteprobabilityrandom-walkgamblers-ruin - DRWHardQuantitative ResearcherPosterior Probability of a Two-Headed Coin After Three Heads
This question tests the application of Bayes' theorem, conditional likelihoods, and posterior probability updates given sequential binary observation data.
Statistics & MathOnsiteProbabilityBayes' TheoremQuantitative Research - DRWHardQuantitative ResearcherProbability the Two Strongest Teams Meet in a 16-Team Knockout Final
Calculate the probability that the two highest-ranked teams face each other in the championship match of a 16-team single-elimination tournament with probabilistic match outcomes.
Statistics & MathOnsiteprobabilitycombinatoricsconditioning - Goldman SachsMediumQuantitative ResearcherEstimate Pi with a Square Cardboard and a Fair Die to 5-6 Decimal Places
Using only a square piece of cardboard and a fair six-sided die, design an experiment that estimates pi empirically, then explain how many trials 5 to 6 correct decimal places would require and how you would get there. It tests Monte Carlo design, bias versus variance, and error analysis.
Statistics & MathTechnical Screen - Goldman SachsMediumQuantitative ResearcherFrom Alpha Signal to Portfolio: Reliability, Regime Dependence and Entropy
Walk through how a quantitative equity team turns a predictive signal into portfolio holdings, then explain how to judge whether the signal is reliable, whether its performance depends on the market regime, and how entropy and mutual information can be used to evaluate signals and portfolios.
Statistics & MathTechnical Screen - Goldman SachsMediumQuantitative ResearcherMinimum-Cost Round Trip Visiting Every City Once with Asymmetric Costs
Given a complete matrix of possibly asymmetric travel costs between up to 16 cities, return the minimum cost of a round trip that starts at city 0, visits every other city exactly once and returns to city 0. The problem tests exact optimization over subsets of cities within tight limits.
Coding & AlgorithmsTechnical Screen - Goldman SachsMediumQuantitative ResearcherTotal Rainwater Trapped Above an Elevation Profile of Unit-Width Bars
Given the heights of adjacent unit-width bars in an elevation profile, compute the total amount of rainwater that stays trapped between them after rain, with water spilling off both open ends. The coding problem tests reasoning about the bars that bound each position and handling up to 100,000 bars efficiently.
Coding & AlgorithmsTechnical Screen - Hudson River TradingMediumQuantitative ResearcherImplement Buffer Parsers and Generic Map Class
This problem tests your ability to safely parse binary data streams from raw memory buffers and implement a type-safe generic wrapper around standard associative containers.
Coding & AlgorithmsOnline AssessmentC++TemplatesBinary-Parsing - J.P. MorganMediumQuantitative ResearcherExplain Daily Signal Construction and a Prediction Workflow
Explain daily quantitative signal construction and prediction at an appropriate level of disclosure, with point-in-time inputs, workflow validation, ownership, and confidentiality boundaries.
Machine LearningTechnical Screen - Jane StreetMediumQuantitative ResearcherDesign a Real-vs-Fake DNA Classifier
A Jane Street Data Scientist technical-screen question on building a classifier that distinguishes real from fake DNA sequences, given a small labeled real/fake set plus a large real-only corpus. It tests sequence representation (k-mers, embeddings, CNN/Transformer), hybrid supervised + anomaly-detection framing, realistic hard-negative generation, evaluation under class imbalance, leakage prevention, threshold calibration, and biological failure modes like reverse-complement symmetry and GC-content shortcuts.
Machine LearningTechnical Screen - Jane StreetMediumQuantitative ResearcherFive Pumpkins Weighed in Every Pair: Find the Total Weight
This question evaluates combinatorial counting, algebraic reasoning, and mental-arithmetic skills by testing understanding of pairwise sums and how individual elements contribute to aggregate totals.
Software Engineering FundamentalsOnsite - Jane StreetMediumQuantitative ResearcherMinimum Common Pairwise Correlation Among Seven Identically Distributed Random Variables
This question evaluates understanding of correlation constraints, covariance/correlation matrix structure, and spectral analysis, combining probability, statistics, and linear algebra competencies within the Machine Learning/Data Science domain.
Machine LearningOnsite - Jane StreetMediumQuantitative ResearcherWhich Dice Game Wins More Often: One Six in 4 Rolls, or Double Sixes in 24 Rolls?
This question evaluates probabilistic reasoning and statistical intuition, focusing on independence of trials, event probability versus expected value, and the ability to compare probabilities of rare events.
Software Engineering FundamentalsOnsite - Jump TradingMediumQuantitative ResearcherMaintain a Fixed-Window Rolling Mean in Constant Time
Implement an efficient sliding-window mean calculation over a stream or sequence of signed integers. Tests sliding window mechanics, constant-time updates, handling integer overflow, and edge cases.
Coding & AlgorithmsOnsiteSliding WindowArraysStreaming - OptiverMediumQuantitative ResearcherDesign dice betting strategy under time limit
This question evaluates probabilistic reasoning, expected-value computation, risk management and staking-strategy concepts including Kelly-style bet sizing, confidence-interval estimation, and return–variance trade-offs in the Statistics & Math domain.
Statistics & MathTechnical Screen - OptiverEasyQuantitative ResearcherFermi Estimation with Confidence Intervals: How Many Houses Does One Year of US Netflix Spending Buy?
This question evaluates a candidate's quantitative estimation and uncertainty-quantification skills, focusing on decomposition into estimable factors, clear articulation of assumptions, calibrated confidence intervals, and updating estimates when new aggregate data are provided.
Machine LearningTechnical Screen - OptiverMediumQuantitative ResearcherIntroduce yourself and justify quant research fit
This question evaluates communication, motivation, collaboration, and quantitative engineering competencies, including domain skills such as low-latency systems, data/ML for signal generation, simulation/backtesting, probabilistic/statistical literacy, and rapid estimation under pressure.
Behavioral & LeadershipTechnical Screen - OptiverEasyQuantitative ResearcherStone Pile Doubling Game: Can One Pile Be Emptied?
This question evaluates a candidate's algorithmic reasoning and discrete-mathematics skills, particularly understanding deterministic state transitions, invariants, and cycle detection in iterative processes.
Coding & AlgorithmsTechnical Screen - OptiverEasyQuantitative ResearcherTrading Game: Expected-Value Betting, Kelly Sizing, and Arbitrage Under Time Pressure
This question evaluates quantitative probability reasoning, expected-value computation, risk-sizing (including Kelly-style sizing), arbitrage recognition, and market-making intuition within a live trading simulation.
Machine LearningTechnical Screen - OtherMediumQuantitative ResearcherOptimal Stopping with a Ten-Sided Die and a Reset
Derive optimal stopping thresholds for a fair ten-sided accumulation game, then account for the continuation value of a one-time zero reset.
Statistics & MathTechnical Screen - Point72MediumQuantitative ResearcherCalibrate the Risk-Aversion Parameter
Calibrate mean-variance risk aversion to target risk and out-of-sample utility while accounting for units, scaling, constraints, and regimes.
Statistics & MathOnline Assessment - Point72MediumQuantitative ResearcherChoose Practical Portfolio Constraints
Specify practical stat-arb constraints for neutrality, exposure, concentration, turnover, liquidity, and borrow, including feasibility checks.
Statistics & MathOnline Assessment - Point72MediumQuantitative ResearcherCompare Missing-Value Strategies for Linear Models
Compare mean, random empirical, and indicator-assisted imputation for linear models under MCAR, MAR, and MNAR without training leakage.
Machine LearningOnline Assessment - Point72MediumQuantitative ResearcherCompare Two Equity Risk Models
Compare two equity risk models out of sample using forecast calibration, residual structure, stability, coverage, regimes, and portfolio impact.
Machine LearningOnline Assessment - Point72MediumQuantitative ResearcherDefend a Quantitative Factor Research Project
Practice defending quantitative factor construction, information coefficient, model selection, backtesting, and research improvements.
Machine LearningTechnical Screen - Point72MediumQuantitative ResearcherDerive Annualized Portfolio Volatility
Derive portfolio volatility from factor covariance and stock-specific variance, then annualize it with consistent horizons and units.
Statistics & MathOnline Assessment - Point72HardQuantitative ResearcherDerive the Optimal Bid in a Two-Bidder Second-Price Auction
Two risk-neutral bidders compete for one item in a sealed-bid second-price auction. Explain the assumptions and derivation clearly, check edge cases, and show how the result changes when those assumptions no longer hold.
Statistics & MathTechnical Screen
DRW
Goldman Sachs
Hudson River Trading
J.P. Morgan
Jane Street
Optiver
Point72