Develop alpha models for credit derivative products and their interactions with other asset classes to generate predictive signals and influence trading strategies.
Requirements
- Proven hands-on experience in alpha research, systematic strategy development, and market microstructure analysis.
- Deep knowledge of credit markets and products (Bonds, CDS, ETFs), including trading protocols such as rolls, basis trades, portfolio hedging, NAV behavior, and cross-asset liquidity dynamics.
- Proficiency in Python and data science libraries (pandas/polars, scikit-learn, matplotlib/plotly).
- Ability to write production-quality code for data ingestion, processing, and real-time visualization.
- Experience with C++ is a plus.
Responsibilities
- Research and develop short- and medium-horizon alpha models for credit derivative products.
- Analyze RFQ dynamics, flows, and liquidity patterns to identify market microstructure inefficiencies that can be systematically captured.
- Build predictive signals linking credit indices with ETFs, equities, and futures, focusing on relationships across risk transfer markets.
- Improve and extend components of the alpha generation framework, including signal libraries, fitters, reporting pipelines, and backtesting engines.
- Design and run backtests to evaluate alpha performance across multiple horizons (intraday to multi-day), incorporating Costs, Slippage, and Liquidity effects.
- Work with engineers, traders, and researchers to deploy alpha models into live trading systems, monitor performance, diagnose issues, and refine models post-deployment.
Other
- Master’s or PhD in a quantitative field (math, physics, statistics, computer science, engineering).
- The opportunity to work alongside best-in-class professionals from over 40 different countries
- Highly competitive compensation package
- Global profit-sharing pool and performance-based bonus structure
- 401(k) match up to 50%
