Master Two Sigma's data-driven interview process with our comprehensive collection of machine learning, data science, statistical modeling, and quantitative reasoning problems. Get hired at one of the world's most innovative quantitative investment firms.
Data analysis, feature engineering, and statistical modeling challenges.
ML algorithms, model selection, and performance optimization.
Advanced statistical concepts and probability theory applications.
Algorithm design, complexity analysis, and optimization problems.
Quantitative finance, risk modeling, and portfolio optimization.
Research methodology, innovation, and problem-solving approaches.
You have a dataset with 1 million samples and 1000 features. How would you approach feature selection and model selection for this high-dimensional problem?
Consider dimensionality reduction techniques, regularization methods, and cross-validation strategies for high-dimensional data.
Use PCA for dimensionality reduction, L1/L2 regularization, feature importance ranking, and k-fold cross-validation with proper train/validation/test splits.
A trading strategy shows a Sharpe ratio of 1.5 over 252 trading days. Test the hypothesis that the true Sharpe ratio is greater than 1.0.
Use t-test for Sharpe ratio with proper assumptions about return distribution and autocorrelation considerations.
Calculate t-statistic = (Sharpe_observed - Sharpe_hypothesis) / SE(Sharpe), where SE(Sharpe) = sqrt((1 + 0.5*Sharpe²)/T). Compare to critical t-value.
You're given market data with price, volume, and volatility. How would you engineer features to predict future price movements?
Consider technical indicators, lagged features, rolling statistics, and domain-specific transformations.
Create features like: price momentum, volume-weighted average price, volatility clustering, moving averages, and regime indicators.
Two Sigma typically has 4-5 rounds: initial phone screen, technical interviews, coding challenges, and final rounds with senior researchers. Each round focuses on different aspects of data science and machine learning skills.
Two Sigma primarily uses Python, R, and SQL for data analysis and machine learning. Python is most common for ML and data science, while R is used for statistical analysis. Strong programming skills and familiarity with ML libraries are essential.
Focus on machine learning algorithms, statistical modeling, data preprocessing, and feature engineering. Practice coding problems in Python and R. Study financial markets and quantitative finance. Prepare for 3-4 months with daily practice in data science and ML concepts.