Quantitative Researcher - Fixed Income Electronic Trading (VP)
- Develop and refine mid-price estimation models using statistical and machine learning techniques.
- Build and optimize autoquoting engines and RFQ response logic for electronic trading.
- Collaborate with trading and technology teams to deploy models into production and monitor performance.
- Conduct backtesting, signal validation, and model calibration using large-scale market and trade data.
- Drive research initiatives across credit, rates, FX, and municipal bonds, contributing to the evolution of the firm's electronic trading strategies.
- Stay abreast of market structure changes and regulatory developments impacting fixed income trading.
- Advanced degree in a quantitative discipline (e.g., Mathematics, Physics, Computer Science, Financial Engineering).
- PhD required for candidates without prior experience on an algorithmic trading desk.
- Master's degree acceptable for candidates with deep hands-on experience in electronic trading.
- Strong proficiency in machine learning, including supervised and unsupervised learning, feature engineering, and model evaluation.
- Programming expertise in Python, C++, or Java, with experience in data analysis and modeling.
- Deep product knowledge in one or more of the following: Credit, Rates, FX, or Municipal Bonds.
- Experience working with large datasets, market microstructure, and real-time trading systems.
- Excellent communication skills and ability to work cross-functionally in a collaborative environment.
- Prior experience in electronic trading, especially within a sell-side fixed income desk.
- Familiarity with RFQ platforms, autoquoting engines, and execution algorithms.
- Exposure to systematic trading strategies, signal-based decision-making, and ML model deployment in production environments.
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