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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 1–13 of 13 results

  • J.P. MorganMediumQuantitative Researcher
    Explain 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 Researcher
    Design 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 Researcher
    Minimum 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
  • OptiverEasyQuantitative Researcher
    Fermi 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
  • OptiverEasyQuantitative Researcher
    Trading 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
  • Point72MediumQuantitative Researcher
    Compare 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 Researcher
    Compare 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 Researcher
    Defend a Quantitative Factor Research Project

    Practice defending quantitative factor construction, information coefficient, model selection, backtesting, and research improvements.

    Machine LearningTechnical Screen
  • Point72MediumQuantitative Researcher
    Encode Categorical Variables Across Model Families

    Choose categorical encodings for linear, tree, and neural models while handling intercept rank, unseen values, and high cardinality.

    Machine LearningOnline Assessment
  • Point72MediumQuantitative Researcher
    Explain Ensemble Learning and Its Main Families

    Explain why combining multiple models can improve predictions. Cover data and labels, leakage-safe features, baselines and model choice, offline evaluation, deployment constraints, monitoring, and drift.

    Machine LearningTechnical Screen
  • Point72MediumQuantitative Researcher
    Explain Transformer Encoder and Decoder Behavior

    This question evaluates knowledge of Transformer architecture and generative language modeling, specifically the encoder versus decoder roles, attention patterns and causal masking, plus mechanisms behind stochastic text generation such as sampling and temperature.

    Machine LearningTechnical Screen
  • Two SigmaMediumQuantitative Researcher
    Daily Temperatures by Town: Linear Models to Predict NYC Temperature

    This question evaluates proficiency in ordinary least squares linear regression (with intercept), basic statistical summaries such as standard deviation and median, and model-evaluation metrics like in-sample mean squared error applied to aligned daily time-series temperature data.

    Machine LearningTechnical Screen
  • VoleonMediumQuantitative Researcher
    Reason About Train and Test Loss Under Regularization and More Data

    Reason about train/test gaps, high losses, regularization strength, and learning curves while separating statistical tendencies from finite-sample guarantees.

    Machine LearningTechnical Screen