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Original posting from Chase- Candidate Experience page
Join JPMorgan Chase’s Private Bank Solutions Investment Quantitative Research team as an Associate specializing in Derivatives Risk Modeling and Analytics. You'll contribute to solving solutions spanning derivatives pricing and risk modeling, factor modeling, Greeks and sensitivity analytics, portfolio-level risk aggregation, stress testing, and scenario analysis across a broad derivatives universe. The team works closely with portfolio managers, derivatives solutions specialists, risk, and lending teams across JPMorgan Chase Wealth Management, as well as partnering with Technology teams to deliver solutions at scale. The quantitative research team is based in New York and Mumbai.
You will be responsible for developing and implementing quantitative models for derivatives risk, valuation, and P&L analytics to enhance our modeling capabilities and expand coverage across OTC and exchange-traded derivatives. You will build deep expertise across multiple derivatives asset classes, including Equity Derivatives (options, variance/volatility swaps, exotic structures), Interest Rate Derivatives (swaps, swaptions, caps/floors), Credit Derivatives (CDS, CDX, tranches), FX Derivatives (options, barriers, accumulators), Commodity Derivatives, and Structured Products (structured notes, autocallables, and other payoff structures).
Job Responsibilities
- Derivatives Risk Modeling: Develop and implement pricing and risk models for vanilla and exotic derivatives across equity, rates, credit, FX, and commodities.
- Greeks & Sensitivity Analytics: Build and maintain sensitivity frameworks capturing delta, gamma, vega, theta, rho, and higher-order Greeks; implement bump-and-reprice and algorithmic differentiation approaches for efficient risk computation.
- P&L Attribution: Develop attribution frameworks isolating contributions from underlying moves, volatility surface changes, time decay, correlation, skew, and basis risk across derivative portfolios.
- Factor Modeling: Contribute to multi-factor risk models that capture key drivers of derivatives portfolios, including implied volatility surface dynamics, correlation structures, term structure movements, and skew behavior.
- Stress Testing & Scenario Analysis: Implement stress testing frameworks for volatility shocks, correlation breakdowns, liquidity dislocations, gap risk, and historical crisis events; support scenario methodologies capturing tail risk, non-linear payoff effects, and path dependency.
- Structured Products Analytics: Develop valuation and risk models for structured notes and bespoke payoffs, including autocallables, barrier products, and range accruals; model embedded optionality and issuer credit risk.
- Research: Conduct empirical research on volatility surface dynamics, correlation modeling, model calibration techniques, and market microstructure; contribute to new risk factor development and model enhancements.
- Validation & Governance: Perform backtesting of pricing models, validate model assumptions against market data, and contribute to comprehensive model documentation in line with governance standards.
- Technology & Data: Partner with Technology to productionize scalable derivatives pricing and risk engines, build APIs, and curate multi-vendor market data (volatility surfaces, curves, correlation matrices).
- Collaboration: Work closely with senior team members and colleagues in New York and Mumbai, collaborate with derivatives solutions specialists, and contribute to a culture of intellectual rigor and continuous improvement.
Qualifications
- Experience: 5+ years of experience in quantitative research or model development focused on derivatives, in an asset management, private bank, or sell-side environment. Exposure to derivatives pricing, risk modeling, or analytics across one or more asset classes.
- Derivatives Knowledge: Strong foundational knowledge of derivatives pricing theory — including Black-Scholes and extensions, familiarity with local/stochastic volatility models (Heston, SABR), interest rate modeling frameworks, and numerical methods (Monte Carlo, PDE, lattice). Understanding of exotic payoff structures, path dependency, and Greeks computation.
- Risk & Portfolio Analytics: Understanding of portfolio-level risk concepts for derivatives, including VaR/CVaR methodologies, factor-based risk decomposition, and sensitivity-based risk aggregation. Awareness of counterparty credit risk concepts (CVA/DVA) is a plus.
- Communication: Ability to clearly communicate quantitative findings to senior team members, portfolio managers, and risk stakeholders; comfort working in a collaborative, cross-functional environment.
- Data & Tools: Familiarity with market data vendors and platforms including Bloomberg, MSCI, or ICE. Exposure to derivatives pricing libraries (QuantLib or equivalent) is a plus.
- Programming: Strong proficiency in Python with experience in numerical computing (NumPy, SciPy), data analysis (pandas), and visualization (matplo