Review and assess AI-generated quantitative research and trading strategy outputs focused on fixed income and rates markets.
Identify and document methodological issues such as lookahead bias, overfitting, and unrealistic transaction cost assumptions.
Evaluate the accuracy of backtesting frameworks, especially with respect to rates-specific mechanics like carry/roll-down.
Scrutinize the statistical rigor of signal construction, factor modeling, and alpha research in the provided outputs.
Deliver detailed written feedback on model assumptions, implementation fidelity, and research soundness.
Collaborate asynchronously with project coordinators by clarifying findings and suggestions through clear communication.
Requirements
Background as a quantitative researcher, analyst,



or consultant with expertise in fixed income or rates markets.
Demonstrated experience building and backtesting systematic rates strategies at a hedge fund, asset manager, or bank.
Advanced proficiency in Python (pandas, numpy, scipy) or R for quantitative research and data analysis.
Strong understanding of yield curve modeling, carry/roll-down analytics, relative value trading, and Treasury futures.
Comfort evaluating research under real-world trading conditions, including transaction costs and liquidity constraints.

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📌 Data Analyst | Remote (Heroica Puebla de Zaragoza)
🏢 Crossing Hurdles
📍 Heroica Puebla de Zaragoza