Design, train, and deploy ML models (deep learning, NLP, reinforcement learning, or large-scale ensemble methods) for alpha generation across equities, futures, and other liquid markets
Own the full pipeline from research and backtesting to live production deployment
Mine large, noisy, non-stationary financial datasets to engineer features and signals that hold up out-of-sample
Collaborate with quant researchers, engineers, and traders to integrate ML-driven signals into existing systematic strategies
Ideal Candidate
2 to 5 years of experience applying machine learning in a quantitative research, trading, or comparable high-stakes production environment
Strong foundations in statistics, applied ML, and time-series/panel data methods; healthy skepticism about overfitting in noisy financial data
Proficiency in Python and/or C++; experience with large-scale data pipelines and model deployment infrastructure
PhD or 2 years of industry experience in ML, statistics, CS, or a related quantitative field a strong plus
