Systematic Credit Trader; IG & HY leading prop trading firm

Role Overview

The successful candidate will take ownership of systematic high-yield market-making and market-taking strategies, participating directly in RFQ workflows and automated pricing while building scalable frameworks that systematize the firm’s credit trading expertise. This position sits at the intersection of trading, research, and engineering, with the mandate to integrate systematic methods into the firm’s core credit business.

You will not only produce predictive signals but also drive the end-to-end strategy pipeline from alpha research and data ingestion through model deployment, execution logic, and live risk management.

• Develop and trade systematic high-yield and IG strategies focused on market making, RFQ response, and liquidity provision across single-name and index credit instruments (cash bonds, CDS, ETFs).
• Design and systematize pricing, quoting, and inventory models to automate RFQ answering, market taking, and spread optimization.
• Build robust frameworks for signal generation, alpha validation, and live model monitoring.
• Collaborate with engineering and infrastructure teams to develop low-latency RFQ systems, execution logic, and post-trade analytics.
• Incorporate systematic processes into core discretionary credit trading books, creating hybrid models that blend data-driven insights with trader intuition.
• Perform detailed transaction cost, liquidity, and slippage analyses to optimize performance in OTC credit markets.
• Work closely with portfolio managers, risk, and operations to ensure robust scaling, capital allocation, and governance of systematic credit strategies.

• 3–8+ years of experience in systematic or hybrid credit trading, preferably within high-yield or investment-grade markets at a proprietary trading firm, hedge fund, or bank electronic trading desk.
• Proven ability to build or improve RFQ and market-making automation for corporate bonds or CDS.
• Hands-on experience in signal research, execution modeling, and risk analytics, full lifecycle strategist capable of going from idea to live strategy.
• Deep understanding of credit market microstructure liquidity tiers, portfolio trading, axe/IOI dynamics, TRACE data, dealer selection, and price discovery.
• Strong programming proficiency in Python (pandas/numpy/scikit-learn); familiarity with C++ or low-latency environments is a plus.
• Comfort with data ingestion, model validation, out-of-sample testing, and production deployment workflows.
• Highly collaborative mindset, capable of working with traders, engineers, and data scientists to integrate systematic components into broader trading frameworks.

• Experience designing execution logic for RFQ aggregation, hit-rate optimization, and quoting engines.
• Knowledge of credit ETFs, index arbitrage, and basis relationships (cash vs. CDS, ETF vs. index).
• Familiarity with fundamental or issuer-level features (balance sheet metrics, leverage, sector risk) and how to systematize them.
• Exposure to machine learning methods for price prediction, liquidity forecasting, or quote ranking.