Quantitative Rates Researcher

Quantitative Rates Researcher

Compensation

Annualized Compensation: $153,600-$288,000 per year

Hourly Rate: $80-$150 per hour

Employment Type: Contractor

Work Arrangement: Remote

The annualized figure is based on 40 hours per week and 48 working weeks per year. Actual earnings will depend on project workload, assignments, and hours worked.

About the Role

We are seeking experienced Quantitative Rates Researchers to evaluate AI-generated quantitative research and trading strategy outputs focused on fixed income and rates markets.

In this role, you will apply your expertise in systematic trading, quantitative modeling, and statistical research to assess the accuracy, rigor, and practical validity of AI-generated work. You will examine research methodologies, identify potential biases, evaluate backtesting frameworks, and provide detailed feedback on model assumptions and implementation.

Your insights will help improve how AI systems reason about quantitative finance and develop research outputs that reflect real-world trading conditions.

Prior AI experience is not required. Strong domain expertise in quantitative research and fixed income or rates markets is the primary focus.

Key Responsibilities

• Review and assess AI-generated quantitative research and trading strategy outputs focused on fixed income and rates markets.

• Identify methodological issues, including lookahead bias, overfitting, data snooping, and unrealistic transaction cost assumptions.

• Evaluate the accuracy and reliability of systematic strategy backtesting frameworks.

• Assess rates-specific mechanics, including carry and roll-down calculations, yield curve modeling, and contract roll conventions.

• Scrutinize signal construction, factor modeling, alpha research, and statistical methodologies.

• Evaluate research assumptions against real-world trading conditions, including transaction costs and liquidity constraints.

• Review the treatment of SOFR and Eurodollar instruments, Treasury futures, and relative value trading strategies.

• Provide detailed written feedback on model assumptions, implementation fidelity, research methodology, and overall analytical soundness.

• Communicate findings, explain methodological concerns, and recommend improvements through clear written and verbal communication.

• Collaborate asynchronously with project coordinators to clarify findings and suggestions.

• Contribute to improving research evaluation processes for greater accuracy and relevance.

Required Qualifications

• Strong quantitative research or analytical expertise in fixed income or rates markets.

• Demonstrated understanding of systematic trading strategies and quantitative research methodologies.

• Advanced proficiency in Python or R for quantitative research and data analysis.

• Experience using Python libraries such as pandas, NumPy, and SciPy, where applicable.

• Strong understanding of statistical rigor, signal construction, factor modeling, and alpha research.

• Knowledge of yield curve modeling, carry and roll-down analytics, and relative value trading.

• Familiarity with SOFR and Eurodollar instruments and Treasury futures.

• Ability to evaluate backtesting methodologies and identify research flaws, including lookahead bias, overfitting, and data snooping.

• Understanding of transaction cost analysis, liquidity assessment, and realistic trading assumptions.

• Exceptional attention to detail and strong written communication skills.

• Ability to explain complex quantitative findings clearly and collaborate effectively in an asynchronous environment.

Preferred Qualifications

• Background as a quantitative researcher, analyst, or consultant specializing in fixed income or rates markets.

• Experience building and backtesting systematic rates strategies at a hedge fund, asset manager, or bank.

• Experience evaluating quantitative models against real-world trading constraints.

• Prior experience reviewing models for compliance with quantitative finance best practices.

• Experience identifying methodological weaknesses and validating quantitative research outputs.

Key Skills

• Quantitative Research

• Fixed Income Analysis

• Rates Markets Expertise

• Python

• R

• Pandas

• NumPy

• SciPy

• Systematic Strategy Backtesting

• Yield Curve Modeling

• Carry and Roll-Down Analytics

• Relative Value Trading

• SOFR and Eurodollar Instruments

• Treasury Futures

• Statistical Analysis

• Factor Modeling

• Alpha Research

• Transaction Cost Analysis

• Liquidity Assessment

• Model Validation

• Lookahead Bias Detection

• Overfitting and Data Snooping Detection

• Data Analysis

• Research Methodology Review

• Technical Writing

• Attention to Detail

• Collaborative Communication

What Success Looks Like

• Quantitative research outputs are evaluated for statistical rigor, methodological accuracy, and practical relevance.

• Backtesting frameworks reflect appropriate rates-market mechanics and realistic trading assumptions.

• Potential sources of bias, overfitting, and unreliable conclusions are clearly identified and documented.

• Model assumptions and implementation details receive thorough, evidence-based scrutiny.

• Written feedback communicates complex findings clearly and supports improvements in AI-generated quantitative research.