The Quantitative Trading & Research (QTR) Equity Derivatives team is looking for a junior quant to focus on exotic products. The objective is to drive and implement analytics, optimization and modeling for Equity Exotic trading, with immediate focus on building robust trade booking, analytics and model validation layers.

As an associate for the Equity Derivatives Exotics QTR team, you will make extensive use of quantitative techniques, including machine learning, to deliver end-to-end solutions for the business. This includes introducing a systematic framework to develop derivative products, strengthen risk and P&L control and facilitate lifecycle management, developing derivative pricing and lifecycle models, as well as identifying and monitoring associated model risks. It is particularly important for this role, that you are a disciplined developer, adhering to the highest standard of development, testing, deployment life cycle, working with the broader QTR team and with technology.

Job responsibilities:

• Develop a framework and key components to develop derivative products including life cycling and model validation, using dependency-graph programming and Python language.
• Model derivative products using C++ - Python hybrid programming to meet business requests.
• Drive payoff innovation using the product design framework and machine learning techniques.
• Streamline product review under the product design framework and provide clear model documentation to facilitate model approvals.
• Evaluate quantitative methodologies including identifying and monitoring model risks associated with derivative valuation models.
• Support trading activities by explaining model behavior, identifying major sources of risk in portfolios and carrying out scenario analyses.

Required qualifications, capabilities, and skills:

• Master or PhD degree in a quantitative field from a top university.
• 1-5 years of experience in derivatives quantitative research.
• Strong programming skills in C++, Python and numerical packages.
• Experience with statistical analysis and machine learning.
• Experience with derivatives pricing models and equity derivatives products.
• Solid understanding of the application of Monte-Carlo simulation and finite-difference PDE in derivative pricing.
• Ability to communicate effectively with business stakeholders.

Preferred qualifications, capabilities, and skills:

• Prior experience in a front-office quantitative research role.
• Experience or good knowledge in dependency-graph programming.
• Knowledge of risk management frameworks and regulatory requirements.

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J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world’s most prominent corporations, governments, wealthy individuals and institutional investors. Our first-class business in a first-class way approach to serving clients drives everything we do. We strive to build trusted, long-term partnerships to help our clients achieve their business objectives.

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J.P. Morgan’s Commercial & Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world.

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Build models and analytics for equity exotics; drive pricing, risk and lifecycle solutions using C++ and Python.