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Quantitative Trading & Research - Global Clearing - Vice President

JPMorganChase · New York, NY · Yesterday
On-siteFinanceFull-time

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.

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.

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