Quantitative Research Intern
About The Position
Exceptional trading emerges where human intuition meets frictionless experimentation. Our platform and processes enable traders to rapidly investigate ideas, identify emergent patterns, and convert insights into live strategies. This synthesis creates a flywheel of discovery — the key to our pursuit of excellence. We are looking for a Quantitative Research Intern to help build machine learning-driven statistical arbitrage and high-frequency trading strategies in U.S. equities. You will work closely with our senior researchers, using large-scale data and quantitative models to predict short-term price movements, design models, and capture market inefficiencies.
Requirements
- Education: Currently pursuing a PhD in a highly quantitative field such as Computer Science, Statistics, Mathematics, Physics, or Engineering.
- Programming: Strong proficiency in Python and its scientific computing stack (Pandas, NumPy, SciPy, scikit-learn).
- Deep Learning: Practical experience building and training RNN (Recurrent Neural Network) and Transformer architectures.
- Machine Learning: Hands-on experience with predictive modeling, including regression, classification, clustering, as well as bagging and boosting algorithms (e.g., XGBoost, LightGBM).
- Behavioral: Strong intellectual curiosity paired with low ego. A relentless drive to find the truth in data, the ability to clearly defend your research methodology, and the eagerness to learn how theoretical models translate to financial markets.
- Competitive Pedigree: Demonstrated success in competitive environments, such as AI/machine learning competitions (e.g., Kaggle) or programming competitions (e.g., ICPC, Putnam).
Responsibilities
- Alpha & Strategy Research: Explore, model, and validate new predictive signals using large, complex datasets.
- Machine Learning Application: Apply advanced machine learning and deep learning architectures to uncover hidden patterns and inefficiencies in financial markets.
- Rigorous Validation: Stress-test your hypotheses, identify hidden biases, and analyze model performance using the team’s backtesting frameworks.
- Collaborative Innovation: Participate in technical discussions and present your research findings to the broader team.
Benefits
- lunch stipends
- great location with amazing colleagues
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What This Job Offers
Career Level
Intern
Education Level
Ph.D. or professional degree
