Quantitative Trading & Research - Quantitative Researcher for Systematic Volatility Trading – Associate / Vice President
- LocationCentral and Western, Hong Kong Island, Hong Kong
- Work typeOn-site
- EmploymentFull-time
- Posted10 November 2026
- SourceJPMorganChase's careers page (Oracle Recruiting)
Live checkFirst seen 2 h ago; still listed on JPMorganChase's careers page just now.
Moves fastWhen JPMorganChase takes a role down early, it is usually about 6 days after posting. Apply soon.
At a glance
- EducationMaster's degree
- Skills and toolsPython, C++, Java, Machine learning
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About the role
Are you interested in derivatives trading, quantitative modelling and building systems that make trading decisions in real time? Join the Quantitative Trading & Research (QTR) Systematic Trading team in APAC. You will help develop systematic trading and portfolio management capabilities across listed and OTC equity derivatives, turning research into automated strategies that run live.
As an Associate / Vice President in the QTR Systematic Trading APAC team, you will work with equity derivatives traders, quantitative researchers and technology partners to research, build and run systematic trading strategies. The role combines model development with a solid understanding of how automated trading systems operate in production. You will own work across the full lifecycle, from research and backtesting to deployment and live performance review. Strong communication and a practical, delivery-focused mindset are essential, as you will be a culture carrier for automated trading in equity derivatives.
Job Responsibilities
- Partner with equity derivatives trading desks to identify and deliver opportunities for systematic trading.
- Research and develop quantitative models and signals for market making, portfolio management and execution.
- Analyse trading performance and transaction costs, and use the results to improve models.
- Design backtesting and simulation frameworks to evaluate strategies under realistic conditions.
- Work with technology teams to take models into production, with appropriate monitoring and controls.
- Support live strategies through calibration, performance review and investigation of production issues.
- Communicate research and results clearly to trading, risk and technology partners.
Required Qualifications, Capabilities, and Skills
- PhD or Master's degree in a quantitative discipline such as mathematics, statistics, computer science, physics, engineering or financial engineering.
- Experience in quantitative research or systematic trading in equity derivatives or a related asset class.
- Solid understanding of options and volatility, including Greeks, implied volatility and risk, and of how trading and hedging decisions turn into P&L.
- Understanding of how automated trading systems work end to end, from data and decision logic to risk controls, execution and monitoring.
- Strong statistical modelling skills, with the ability to frame trading questions as data and modelling problems and to judge model risk.
- Strong Python skills with numpy, pandas and the scientific computing stack, and good software practice for research code that goes into production.
- Ability to work with large trading and risk datasets from several sources and reach robust conclusions from them.
- Clear written and verbal communication, including explaining complex models, their assumptions and their limitations.
- A practical, ownership-driven mindset and an interest in seeing models run in monitored live trading.
Preferred Qualifications, Capabilities, and Skills
- Experience with client quoting, market making or portfolio management of option and warrant books.
- Experience with event-driven or stateful trading platforms, messaging systems, order management or execution systems.
- Familiarity with execution algorithms and transaction cost analysis.
- Knowledge of kdb+/q or similar time-series databases.
- Experience with risk limits, pre-trade controls or regulatory requirements for automated trading.
- Experience with C++, Java or production software engineering.
- Exposure to machine learning or optimisation methods applied to trading signals or portfolio management.
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