•Review and evaluate quantitative research outputs and systematic trading strategies focused on fixed income and interest rate markets.
•Identify research and backtesting issues, including lookahead bias, overfitting, data snooping, and unrealistic transaction cost assumptions.
•Evaluate backtesting frameworks, including rates-specific mechanics such as carry, roll-down analytics, and contract roll conventions.
•Assess the statistical rigor of signal construction, factor modeling, and alpha research.
•Review yield curve models, relative value strategies, and interest rate trading instruments.
•Validate model assumptions, implementation accuracy, and research methodologies.
•Evaluate strategies under realistic market conditions, accounting for transaction costs, liquidity constraints, and execution assumptions.
•Provide detailed written feedback with actionable recommendations to improve research quality and accuracy.
•Collaborate asynchronously with project coordinators to clarify findings and communicate technical insights.
•Contribute to improving quantitative research evaluation methodologies.
Required Skills and Qualifications
•7+ years of experience in quantitative research, quantitative analysis, or quantitative finance.
•Strong expertise in fixed income analysis and interest rate markets.
•Advanced proficiency in Python or R for quantitative research and data analysis.
•Hands-on experience with Python libraries such as Pandas, NumPy, and SciPy.
•Strong understanding of systematic trading strategy development and backtesting.
•Experience with yield curve modeling and carry/roll-down analytics.
•Knowledge of relative value trading strategies and interest rate market instruments.
•Familiarity with SOFR and Eurodollar instruments, as well as Treasury futures.
•Strong knowledge of statistical analysis, factor modeling, and alpha research.
•Understanding of transaction costs, liquidity constraints, and realistic trading assumptions.
•Ability to identify lookahead bias, overfitting, data snooping, and other research methodology flaws.
•Excellent analytical, written communication, and problem-solving skills.
•Ability to communicate complex quantitative concepts clearly and work effectively with distributed teams.
