Executive Director - Quantitative Research Principal Software Engineer

If you are looking for a game-changing career, working for one of the world's leading financial institutions, you’ve come to the right place. As a Principal Software Engineer at JPMorgan. Chase within the Asset & Wealth Management, Derivatives Platform Team, you provide expertise and engineering excellence as an integral part of an agile team to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way.

Leverage your advanced technical capabilities and collaborate with colleagues across the organization to drive best-in-class outcomes across various technologies to support one or more of the firm’s portfolios. As a Principal Quantitative Research / Quant Developer within the derivatives platform team in Asset Management, you will build and enhance pricing, risk, and analytics capabilities for derivatives across asset classes on the firm's Athena (Python) platform.

Based in New York, NY, you will partner closely with portfolio managers and traders, developing robust, production-quality models and tools that directly support the investment and risk management needs of the business.

Job responsibilities

Develop and implement pricing models, risk analytics, and quantitative tools for derivatives across rates, credit, and equities Build and enhance analytics on the Athena (Python) platform, ensuring robust testing, clear documentation, and production-quality delivery Partner closely with portfolio managers and traders to translate investment and risk workflows into quantitative solutions Provide analytical support to internal clients by troubleshooting and resolving model- and analytics-related issues Contribute to the derivatives trade lifecycle capabilities, including pricing, risk aggregation, and hedging analytics Architects and governs agentic AI-enabled engineering workflows (using enterprise-authorized tools within the work environment) to improve delivery speed, code quality, and operational outcomes at scale (e.

g., AI-driven PR review assistance, test generation/maintenance, release readiness checks, incident triage and root-cause acceleration), while defining guardrails for validation, security, resiliency, and reuse across teams. Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation at scale.

Creates durable, reusable software frameworks that are leveraged across teams and functions Influences leaders and senior stakeholders across business, product, and technology teams Required qualifications, capabilities, and skills Formal training or certification on software engineering concepts and 10+ years applied experience Advanced degree (Master's or Ph.

D.) in a quantitative discipline such as Mathematics, Physics, Statistics, Engineering, Quantitative/Financial Engineering, or Computer Science Strong quantitative background with proven experience in derivatives across one or more asset classes — rates, credit, and equities Deep understanding of derivatives pricing, risk, and analytics of financial products, including option pricing theory Strong foundation in stochastic calculus, probability theory, and numerical methods Strong programming and quant development skills in Python, with the ability to deliver production-ready solutions Proven track record working as a quant developer and collaborating closely with quants, traders, or portfolio managers, with the ability to face off to the business Excellent communication skills with the ability to engage effectively with front-office stakeholders Preferred qualifications, capabilities, and skills Experience with the JPMorgan.

Chase Athena platform; experience with comparable industry platforms such as Quartz or Beacon is also highly valued for candidates without Athena knowledge Working knowledge of Java, C++, or C# Prior exposure to a front-office quantitative research or trading environment Familiarity with the derivatives trade lifecycle across multiple asset classes