Join a team led by former Citadel professionals, focused on developing and trading systematic mid-frequency equities strategies.
The mandate centers on original alpha research, combining statistical modeling with selective applications of machine learning to uncover predictive relationships in equity markets. You’ll contribute across the research process, from forming hypotheses and developing signals to implementing and refining live strategies.
The research
Develop and test alpha signals across mid-frequency equities, exploring cross-sectional and time-series relationships.
Apply statistical and machine-learning methods to feature engineering, signal combination, and predictive modeling.
Conduct rigorous backtesting, accounting for transaction costs, turnover, market impact, and signal decay.
Evaluate out-of-sample robustness and performance across different market environments.
Collaborate on production implementation and use live results to inform further research.
Your background
Quantitative research experience in systematic equities, ideally within mid-frequency trading or statistical arbitrage.
A strong foundation in statistics, financial data analysis, and predictive modeling.
Experience developing alpha signals and working with large equity datasets.
Strong Python skills; C++ experience is a plus.
Practical exposure to machine learning, with an understanding of overfitting, data leakage, and model validation.
Experience taking research into live trading and demonstrating its contribution to strategy performance is particularly relevant.
Based in New York. Message me for more details or a confidential conversation.
