Join our global Quantitative Trading & Research (QTR) team, where you'll apply your expertise in Derivatives Modelling, Financial Engineering, Data Science, and Quantitative Development. As part of JP Morgan's leading QTR Group, you'll innovate with unique analytics and mathematical models, enhancing business practices through automation. We develop advanced models and methodologies to support the Clearing business, utilizing the Athena quant platform for comprehensive trade and risk management across all asset classes.
Job summary:
As a Vice President Quantitative Researcher in the Quantitative Trading & Research (QTR) Global Clearing team, you will lead the design, delivery, and governance of risk and pricing analytics and models across F&O and OTC derivatives, with a primary focus on risk analytics, Initial Margin (IM) methodology, and production execution. You will set technical direction and partner closely with the Margin Trading desk, Technology, and Product Development to ship high-impact solutions. You will also shape our data-led strategy by applying state-of-the-art machine learning to transform risk management and automation across the investment bank.
Job responsibilities
- Own delivery of front-office risk/pricing analytics and margin solutions using internal derivatives libraries, ensuring robust, performant outcomes across D1, F&O, and OTC products; define multi-quarter roadmaps and drive continuous improvement.
- Lead end-to-end initiatives—from problem framing and hypothesis design through prototyping, backtesting, and scalable production deployment—partnering with Trading, QR peers, Technology, and Product to deliver measurable business impact.
- Design and enhance margin and derivative models, including methodology selection, calibration, numerical schemes, benchmarking/backtesting, documentation, and alignment with model risk governance.
- Serve as model owner: manage roadmaps, controls, monitoring/alerts, change management, and responses to Model Risk, Audit, and regulatory reviews; ensure explainability and transparency of assumptions, limitations, and model performance.
- Build and productionize analytics that advance intraday/EoD automation (services, APIs, pipelines) with clear SLOs/SLA, observability, reliability engineering practices, and tight integration into trading/risk platforms.
- Provide technical leadership and mentorship; conduct code/method reviews, establish research engineering best practices (testing, CI/CD, reproducibility), and develop team capability.
- Communicate complex quantitative concepts to non-technical audiences; influence product roadmaps, prioritization, and resourcing via data-driven analysis, scenario studies, and clear articulation of trade-offs.
- Lead development of ML/AI solutions end-to-end (feature engineering, model training/validation, MLOps, monitoring and drift management) with rigorous controls, documented governance, and demonstrable business value.
- Uphold standards for documentation, reproducibility, traceability, and SDLC; ensure compliance with internal policies for model risk management and data governance.
Required qualifications, capabilities, and skills
- Advanced degree (PhD, MSc, or equivalent) in Mathematics, Physics, Statistics, Computer Science, or a related quantitative field.
- 5+ years of front-office quant experience supporting trading/risk in F&O and/or OTC derivatives, with a track record of production delivery and close trader partnership.
- Deep knowledge of listed and OTC derivatives; strong understanding of risk/P&L attribution, sensitivities/Greeks, model assumptions/limitations, and market microstructure.
- Experience with front-office platforms such as SecDB, Athena, Quartz, or equivalent.
- Strong programming in Python and/or C++; experience architecting maintainable, testable, high-performance codebases and extending large-scale libraries; proficiency in numerical methods and performance tuning.
- Proven experience designing, calibrating, and maintaining IM/pricing models (e.g., curve construction, volatility surfaces, credit/rates models, margin frameworks), including performance monitoring and backtesting.
- Experience delivering production services with Technology partners (APIs, packaging, CI/CD, containerization, logging/monitoring); familiarity with data engineering and compute frameworks.
- Excellent quantitative problem-solving; able to decompose ambiguous problems, select appropriate methods, and communicate uncertainty and trade-offs clearly.
- Outstanding communication and stakeholder management; ability to influence across QR, Trading, Technology, and Product.
- Demonstrated mentorship or team leadership experience, including setting technical direction, conducting reviews, and managing priorities under pressure.
Preferred qualifications, capabilities, and skills
- Expertise in curve building (multi-curve frameworks), volatility surface modeling/calibration (e.g., SABR, Heston, local/stochastic volatility), and numerical methods (PDE/FDM, Monte Carlo, adjoint/automatic differentiation).
- Experience with market risk, time-series/stress analytics, model risk governance, and regulatory expectations for pricing/risk models.
- Hands-on ML/AI for quant finance (signal extraction, surrogate modeling, anomaly detection), including MLOps, drift monitoring, and explainability.
- Knowledge of portfolio optimization, hedging algorithms, execution analytics, and transaction cost modeling.
- Familiarity with distributed computing and market data tooling (e.g., kdb+/q, SQL), and performance engineering for large-scale simulations.
- Contributions to research (internal notes, publications, patents, conference talks) and engagement with the open-source scientific computing ecosystem.