Remote | Quantitative Rates Researcher — $80–$150/hour

24-Mag•New York, NY

1d•Remote

About The Position

24-MAG LLC is seeking experienced quantitative rates research professionals for a remote freelance/contract consulting engagement supporting advanced AI training and evaluation initiatives. The work involves reviewing AI-generated quantitative research, evaluating systematic trading strategies, and assessing the methodological accuracy of financial models and backtesting frameworks. The focus is on fixed income and interest rate markets, statistical validation, yield curve analysis, algorithmic trading methodologies, and providing structured technical feedback. Prior experience with AI training projects is not required. Assignments may include examining backtesting frameworks, reviewing financial models, and assessing statistical analyses. Deliverables include structured research evaluations, methodological assessments, and detailed written feedback. The engagement is fully remote and offers compensation of $80–$150 per hour. The posting states that 100 openings are available.

Requirements

Professional background as a Quantitative Researcher, Quantitative Analyst, Systematic Trading Researcher, or Quantitative Finance Consultant.
Strong expertise in fixed income markets, interest rate products, and systematic rates strategies.
Demonstrated experience developing, evaluating, or backtesting quantitative trading strategies.
Advanced proficiency in Python, particularly pandas, NumPy, and SciPy, or R for quantitative research and data analysis.
Strong understanding of yield curve modelling and interest rate market dynamics.
Practical knowledge of carry and roll-down analytics and their application to fixed income strategies.
Familiarity with relative value trading approaches in rates markets.
Understanding of SOFR/Eurodollar instruments and relevant market conventions.
Experience analysing Treasury futures and contract roll mechanics.
Strong knowledge of statistical modelling, factor construction, and quantitative alpha research.
Ability to identify lookahead bias, overfitting, data snooping, and related methodological issues.
Experience assessing systematic strategy performance under realistic market conditions.
Understanding of transaction cost analysis, liquidity constraints, and execution assumptions.
Familiarity with quantitative model validation and research quality assurance practices.
Strong analytical reasoning, statistical rigour, and quantitative problem-solving capabilities.
Ability to independently evaluate the methodological soundness of complex financial research.
Excellent written communication skills, including the ability to explain complex statistical and financial concepts clearly.
Strong attention to detail when reviewing research assumptions, calculations, and model implementations.
Ability to collaborate effectively through asynchronous written feedback and technical discussions.

Nice To Haves

Experience working in a hedge fund, asset management firm, investment bank, or comparable quantitative research environment is preferred.
Experience reviewing quantitative models against established financial research standards is advantageous.
Experience developing systematic rates strategies at a hedge fund, asset manager, or investment bank is particularly relevant.

Responsibilities

Review AI-generated quantitative research involving fixed income securities and interest rate markets.
Evaluate analytical methodologies used in rates trading and systematic investment strategies.
Assess yield curve models, interest rate dynamics, and financial market assumptions.
Review quantitative analyses involving SOFR/Eurodollar instruments, Treasury futures, and related rates products.
Identify weaknesses in market reasoning, research assumptions, and quantitative methodology.
Evaluate backtesting frameworks for systematic fixed income and rates trading strategies.
Review strategy implementations for consistency with trading rules and market conventions.
Assess carry and roll-down calculations, futures contract rolls, and rates-specific backtesting mechanics.
Evaluate transaction cost treatment, execution assumptions, and liquidity constraints in backtests.
Identify unrealistic backtesting assumptions and recommend technically appropriate improvements.
Review statistical methodologies used in signal construction, factor modelling, and quantitative alpha research.
Assess statistical significance, robustness, and consistency of trading signals.
Identify issues involving lookahead bias, overfitting, and data snooping.
Evaluate model assumptions, research design, and interpretations of quantitative findings.
Review quantitative implementations using Python, R, or comparable research environments.
Review data processing and analytical workflows involving pandas, NumPy, SciPy, and related tools.
Evaluate financial models for implementation accuracy and consistency with documented methodologies.
Assess research outputs involving yield curves, relative value opportunities, and systematic trading signals.
Identify programming inconsistencies, calculation errors, and methodological weaknesses.
Prepare detailed written evaluations of AI-generated quantitative research and trading strategy outputs.
Document methodological errors, unrealistic assumptions, and recommended analytical corrections.
Assess research quality against quantitative finance and model validation practices.
Communicate complex financial and statistical findings clearly to project stakeholders.
Collaborate asynchronously with project coordinators to refine evaluation criteria and improve research assessment quality.