Quantitative Trading & Research - Systematic Trading - Associate

JPMorgan Chase & Co.•New York, NY

11h•$150,000 - $200,000

About The Position

As a Quantitative Trading & Research (QTR) team member, you will drive innovation across the vol trading ecosystem by applying advanced data analytics, statistical modeling, and machine learning. Join our global team and leverage your skills to shape the future of financial markets. We offer comprehensive training and growth opportunities to enhance your skills and advance your career. Our diverse team supports a wide range of business functions, providing a unique environment for professional development. We are committed to accommodating diverse needs and fostering an inclusive workplace.

Requirements

You have a strong quantitative background, as well as practical problem-solving skills.
You have direct working knowledge of signal research with market data and other financial data, alpha capture, and risk warehousing, preferably in equity derivatives.
You like working closely with trading desks, understanding their business, and have a strong mind-set of ownership to have an impact on the way they operate.
You demonstrate proficiency in code design and programming skills, with primary focus on Python, KDB, C++ or Java in a commercial environment.
You have practical data analytics skills on real data sets gained through hands-on experience, and can handle and analyze complex, large scale, high-dimensionality data from various sources.
You quickly grasp business concepts outside immediate area of expertise and adapt to rapidly changing business needs.
You think strategically and creatively when faced with problems and opportunities. You always look for new ways of doing things.
Your excellent communication skills, both verbal and written, can engage and influence partners and stakeholders.

Nice To Haves

Strong graduate degree (MS or PhD) in a quantitative field (Computer Science, Financial Engineering, Mathematics, Physics, Statistics, Economics, …).
Strong expertise in statistics and machine learning in financial industry.
Robust testing and verification practice.
Direct experience with electronic trading, and knowledge of trading algorithms.
3 to 5 years’ experience in finance: market making, electronic trading, trading strategies (high to low frequency: market making, statistical arbitrage, option trading…), or derivatives pricing and risk management.
Knowledge of equity derivatives and volatility products is a plus.
Plus: experience leveraging AI for research and engineering workflows, and familiarity with productionizing AI (repeatable pipelines, evaluation/monitoring, model risk awareness) and using AI agents professionally.

Responsibilities

Work closely with trading to build end-to-end design and implementation of daily and intraday signal research and deployment infrastructure, with special focus on equity derivatives / Systematic derivatives.
Contribute from idea generation to production implementation: perform research, design prototypes, implement alpha signals and systematic strategies; support daily usage, monitor performance, and iterate based on live feedback.
Research and model equity options and volatility dynamics (e.g., surface arbitrage, term structure, skew, dispersion, event risk, RV) and translate insights into deployable systematic strategies.
Develop and maintain robust backtesting, attribution, and regime analysis frameworks tailored to derivatives PnL drivers.
Build models that integrate fundamental, quantitative, and microstructure features to support risk internalization and/or risk warehousing, using statistics, machine learning, or heuristics as appropriate.
Partner with the business on alpha capture, risk recycling, hedging design, and position/risk management for derivatives strategies (including Greeks and scenarios).
Collaborate broadly with QTR teams across regions to build reusable research libraries, tooling, and standardized workflows for experimentation, deployment, and monitoring.
(Plus) Leverage AI/ML and modern AI tooling to accelerate research and improve developer productivity, with an understanding of AI productionization (model governance, evaluation, monitoring, and safe professional use of AI agents).