Quant Trading Analyst

Research, design, and test predictive financial models, alpha signals, and systematic trading strategies using quantitative methods, statistical analysis, and machine learning techniques in Python. Design and implement portfolio construction and optimization frameworks integrating alpha forecasts, risk models, and transaction cost analysis to maximize risk-adjusted returns. Collaborate with portfolio managers and trading desks to identify investment opportunities, conduct research, and translate analytical findings into executable trading strategies. Build quantitative models, analytical tools, and risk management systems to enhance the firm's research capabilities and support trade execution analysis, performance attribution, and infrastructure improvements. Develop machine learning models to forecast asset returns, market microstructure, trading behaviors, risk exposures, and transaction costs using large-scale financial datasets from market data providers and alternative sources. Requirements:Masters degree (US or foreign equivalent) in Finance, Financial Engineering, Statistics, Mathematics or a related field and three (3) years of experience in the position offered or in a related role* OR Bachelors degree (US or foreign equivalent) in Finance, Financial Engineering, Statistics, Mathematics, or a related field and five (5) years of experience in the position offered or in a related role* All of the required experience must have included experience with: developing machine learning models for alpha signal generation using ensemble methods and gradient boosting frameworks in production trading environments; implementing portfolio optimization algorithms incorporating multi-factor risk models, predictive transaction cost models, and trading condition constraints; conducting risk factor decomposition analysis to quantify systematic exposures and attribute portfolio performance; designing simulation frameworks that account for realistic trading conditions; analyzing market microstructure patterns including closing auction dynamics and intraday volatility for predictive modeling; engineering production-grade trading signals by integrating alternative and traditional financial data using scalable Python research infrastructure; and processing institutional equity trading knowledge. * This role entails hybrid work, with time split between working in our New York, NY office and flexibility to telecommute from another U.S. location. To apply: E-mail rsum to: [email protected].