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About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
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Executive Director - Quantitative Research Principal Software Engineer joins the Asset & Wealth Management Derivatives Platform Team to deliver stable and scalable technology products. This role involves building and enhancing pricing, risk, and analytics capabilities for derivatives across rates, credit, and equities on the Athena platform. The engineer will develop production-quality models and tools while collaborating with portfolio managers and traders to translate investment workflows into quantitative solutions. Key responsibilities include managing the derivatives trade lifecycle, including hedging analytics, and architecting agentic AI-enabled engineering workflows to improve code quality and delivery speed. Required skills include an advanced degree in a quantitative discipline, over ten years of experience, and proficiency in Python, with additional knowledge of Java, C++, or C#. The role requires deep expertise in stochastic calculus, probability theory, and numerical methods for financial products.
Develop and implement pricing models, risk analytics, and quantitative tools for derivatives across rates, credit, and equities.
Build and enhance production-quality analytics on the Athena platform using Python.
Translate investment and risk workflows from portfolio managers and traders into technical quantitative solutions.
Provide analytical support by troubleshooting and resolving model and analytics issues for internal clients.
Contribute to derivatives trade lifecycle capabilities including pricing, risk aggregation, and hedging analytics.
Architect and govern agentic AI-enabled engineering workflows to improve delivery speed and code quality.
Create durable, reusable software frameworks that are utilized across multiple teams and functions.
What we're looking for
Formal training or certification on software engineering concepts and 10+ years of 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 including 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 to deliver production-ready solutions.
Proven track record as a quant developer collaborating with quants, traders, or portfolio managers.
Experience with the Athena platform, Quartz, or Beacon (preferred); knowledge of Java, C++, or C# (preferred).
C++
Java
Python
kdb+/q
SQL
CUDA
OpenCL
Boost
QuantLib
NumPy
Pandas
Linux
Git
CI/CD
Monte Carlo Simulations
Finite Difference Methods
Stochastic Calculus
Jira
System Design
Data Platforms
AI
Conversational AI
Text Summarization
Performance Optimization
Scalability
Observability
Code Review
Architecture
Platform Modernization