Job Title: VP - Quantitative Researcher, Credit Market Making (IG Credit / Munis)
Location: New York, NY
Division: Credit Quantitative Research - Electronic Trading
Level: VP (Associate level may be considered for exceptional candidates)
Team Overview:
The Credit Quantitative Research team is a core part of the firm’s electronic trading buildout, supporting systematic market making across Credit flow products. The team works closely with traders and technologists to develop real-time analytics, pricing models, and decision-support tools for products including Investment Grade (IG) Corporate Bonds, Municipal Bonds, Leveraged Loans, and Credit Indices.

We apply a scientific and data-driven approach to trading, combining market microstructure expertise with advanced statistical modeling and machine learning. Our work directly powers the firm’s electronic quoting and execution capabilities, and we are expanding the team with two new roles focused on:

• IG Credit Market Making
• Munis Market Making

Key Responsibilities:

• Mid/Reference Price Modeling: Use statistical and machine learning techniques to develop real-time mid/reference price models for corporate bonds and municipal products.
• Data Analytics & Infrastructure: Build and maintain analytics libraries and systems that support pricing, liquidity, and execution decisions across Credit flow products.
• Model Research & Deployment: Conduct research, backtesting, and performance analysis of models; deploy production-grade solutions in collaboration with trading and technology teams.
• Cross-Functional Collaboration: Partner with traders, market makers, and technologists to improve workflows, enhance model explainability, and drive adoption of analytics tools.
• AI/ML Integration: Explore and apply modern AI/ML techniques to improve model robustness, scalability, and predictive power, especially in low-touch and systematic trading contexts.
• Business Alignment: Ensure models and tools are aligned with trading objectives and contribute meaningfully to P&L and risk management.

Qualifications:

• Advanced degree (PhD or Master’s) in a STEM field such as Financial Engineering, Applied Mathematics, Computer Science, or Physics.
• Strong background in statistics, probability, and machine learning; familiarity with techniques such as parameter optimization, regularization, neural networks, and Gaussian processes.
• Hands-on experience with real-world datasets and practical data analysis.
• Deep understanding of Linear Credit instruments (Bonds, Loans, CDSs); experience with Municipal products is a plus.
• Strong programming skills in Python (pandas, NumPy, scikit-learn, TensorFlow, Spark, etc.); experience with C++ or Java for production systems is preferred.
• Experience with reactive programming and real-time analytics systems is a plus.
• Excellent communication and presentation skills; ability to summarize complex analysis for trading and management audiences.
• Proactive, detail-oriented, and comfortable working in a high-pressure, fast-paced environment.

Key Responsibilities:

• Mid/Reference Price Modeling: Use statistical and machine learning techniques to develop real-time mid/reference price models for corporate bonds and municipal products.
• Data Analytics & Infrastructure: Build and maintain analytics libraries and systems that support pricing, liquidity, and execution decisions across Credit flow products.
• Model Research & Deployment: Conduct research, backtesting, and performance analysis of models; deploy production-grade solutions in collaboration with trading and technology teams.
• Cross-Functional Collaboration: Partner with traders, market makers, and technologists to improve workflows, enhance model explainability, and drive adoption of analytics tools.
• AI/ML Integration: Explore and apply modern AI/ML techniques to improve model robustness, scalability, and predictive power, especially in low-touch and systematic trading contexts.
• Business Alignment: Ensure models and tools are aligned with trading objectives and contribute meaningfully to P&L and risk management