Credit - Systematic Market Making - Associate
On-site · Singapore, Singapore
Job Summary
Systematic Market Making – Associate in Singapore: design and scale automated pricing and execution for corporate bonds and ETFs across Asia. Build and run production trading tools in Python/Java, develop pricing models, back-test hedging strategies, and collaborate with trading, sales, technology, and risk teams to ensure governance and operational excellence. Responsibilities include automating day-to-day workflows for US IG bond trading across Asia, constructing baskets for portfolio creation/redemption, managing intraday and end-of-day risk, validating models, conducting performance studies, and driving production stability and change management. Required: 3 years in quantitative trading or market making for fixed income, risk management experience, a CS or quantitative degree, production trading systems delivery, ETF/portfolio trading experience, Python and Java proficiency, knowledge of credit bond pricing, machine learning applicable to time-series, and familiarity with market-data/time-series tech; plus collaboration across regions.
Required Qualifications
- 3 years experience in quantitative trading, electronic market making, or systematic execution for fixed income markets
- Experience managing risk for a bond market-making book with clear understanding of limits and control frameworks
- Bachelor's Degree in Computer Science or a quantitative discipline with emphasis on simulation or comparable quantitative methods
- Experience delivering production trading systems end-to-end: research, implementation, testing, deployment, and support
- Experience in ETF primary market activity or portfolio trading for corporate bonds, including basket construction and operational execution
- Proficiency in Python and Java, including asynchronous and event-driven programming; experience with testing, CI/CD, and monitoring in production
- Knowledge of credit bond pricing and analytical models used for valuation and execution
- Academic and practical grounding in machine learning applied to time-series or market modeling
- Familiarity with market data and time-series technologies (e.g., kdb+/q) and distributed systems
- Experience building low-latency or large-scale systems; additional languages (C/C++) are a plus
- Strong skills in time-series analysis, optimization, and machine learning; experience with back-testing and model performance monitoring
- Experience collaborating with cross-functional teams across regions and time zones
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