Quantitative Rates Researcher

OpenTrain AI, Inc.; United Arab Emirates; Remote; AED 405,000 - 759,000; Part time

Job summary

OpenTrain AI, Inc. is seeking a part-time contractor to evaluate AI-generated quantitative research and systematic trading outputs for fixed income and rates markets.

The work requires careful judgment about research methods, backtesting, implementation, and real-world trading conditions. You will write clear feedback, explain complex findings, assess issues such as lookahead bias, data snooping, and transaction costs, and help improve evaluation processes.

Qualifications

• Strong quantitative finance experience in fixed income or rates markets.
• Experience building and backtesting systematic rates strategies.
• Proficiency with Python (Pandas, NumPy, SciPy) or R for quantitative research.

Responsibilities

• Review AI-generated research and trading strategies focused on fixed income or rates markets.
• Identify lookahead bias, overfitting, data snooping, and unrealistic transaction cost assumptions.
• Evaluate backtests including carry, roll-down mechanics, and contract roll conventions.
• Provide detailed written feedback on assumptions, implementation accuracy, and research quality.
• Explain findings clearly in writing and conversation, contributing to evaluation processes.

Skills

Quantitative finance

Python (Pandas, NumPy, SciPy)

R

Fixed income

Rates markets

Backtesting

Model validation

Tools

Python

Job description

The Work

You will evaluate AI-generated quantitative research and systematic trading strategy outputs for fixed income and rates markets. The work calls for careful judgment about research methods, backtesting, implementation, and real-world trading conditions.

You will write clear feedback on whether the research is sound, explain complex findings, and help improve how quantitative research is evaluated.

• Review AI-generated research and trading strategies focused on fixed income and rates markets.
• Find issues such as lookahead bias, overfitting, data snooping, and unrealistic transaction cost assumptions.
• Evaluate backtests, including carry, roll-down mechanics, and contract roll conventions.
• Review signal construction, factor models, alpha research, and statistical rigor.
• Assess liquidity, transaction costs, and other real-world trading conditions.
• Give detailed written feedback on assumptions, implementation accuracy, and research quality.
• Explain findings and recommendations clearly in writing and conversation.
• Contribute to more accurate and relevant research evaluation processes.

What It Pays and Takes

This is a part-time contractor opportunity for specialized AI training and research evaluation work. The listing is marked entry level, while the role requires strong quantitative finance experience in fixed income or rates markets.

• Pay: $80 to $150 per hour.
• Work type: Part-time contract work.
• Location: Open to candidates in the countries specified for this role, including the United States, Canada, the United Kingdom, India, and Singapore.
• Language: English-language work.
• Professional background as a quantitative researcher, analyst, or consultant in fixed income or rates markets.
• Experience building and backtesting systematic rates strategies at a hedge fund, asset manager, or bank.
• Advanced Python skills with pandas, NumPy, and SciPy, or R skills for quantitative research and data analysis.
• Strong knowledge of yield curve modeling, carry and roll-down analytics, relative value trading, SOFR or Eurodollar instruments, and Treasury futures.
• Ability to assess research with realistic transaction costs and liquidity constraints.
• Exceptional attention to detail and the ability to explain complex quantitative findings in concise feedback.
• Experience with quantitative finance best practices, model validation, methodological review, and the practical limits of systematic rates research is helpful.

About AI Training Work

AI training work uses human review to help improve artificial intelligence systems, including by evaluating research, written answers, and other model outputs. People with specialized knowledge are needed to judge whether those outputs are accurate, useful, and grounded in real-world practice.