Fixed Income Quant Analyst: Backtesting & Research Quality

Crossing Hurdles; Indonesia; On-site; IDR 300,000,000 - 600,000,000; Full time

Job summary

Crossing Hurdles is seeking a quantitative researcher to review AI-generated research and trading signals for fixed income and rates markets. You will assess methodological soundness and backtesting fidelity, focusing on carry/roll-down and liquidity considerations.

You will document issues, provide clear feedback, and collaborate asynchronously with project coordinators to improve model assumptions and research quality.

Qualifications

• Background as a quantitative researcher, analyst, or consultant with fixed income or rates experience.
• Experience building and backtesting systematic rates strategies at a hedge fund, asset manager, or bank.
• Strong Python or R for quantitative research and data analysis.

Responsibilities

• 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.

Skills

Python

R

Backtesting

Yield curve

Tools

Pandas

Job description

Crossing Hurdles is seeking a quantitative researcher to review AI-generated research and trading signals for fixed income and rates markets. You will assess methodological soundness and backtesting fidelity, focusing on carry/roll-down and liquidity considerations.

You will document issues, provide clear feedback, and collaborate asynchronously with project coordinators to improve model assumptions and research quality.