2027 Quantitative Research Markets Analyst Program – Off-Cycle Internship – London

About the role

This off-cycle London internship joins JPMorganChase’s Quantitative Trading & Research Markets team. You will apply quantitative modeling, statistical research, data analytics, and programming to systematic trading, financial engineering, valuation, hedging, portfolio optimization, and market-making activities while collaborating with traders, technologists, sales, marketing, and risk managers.

Responsibilities

• Develop and maintain mathematical models, algorithms, methodologies, and supporting infrastructure.
• Value and hedge financial transactions, ranging from flow products to complex derivatives.
• Conduct quantitative research and alpha research supporting trading strategies and decision-making.
• Collaborate with trading teams to apply research insights to practical market applications.
• Calibrate model parameters and optimize financial instrument pricing to support growth and market share.
• Manage risk in existing portfolios and partner across quantitative, technology, trading, marketing, and risk teams.

Program

• Receive hands-on experience, relevant skills training, and professional networking opportunities.
• Expert instructors and JPMorganChase professionals provide technical and practical training.
• Successful participants may receive full-time employment offers based on individual achievements.

Required Qualifications

• Be enrolled in a relevant bachelor’s or master’s program, including mathematics, statistics, physics, engineering, or computer science.
• Graduate between September 2026 and March 2028.
• Demonstrate programming experience in Python, C++, or another programming language.
• Demonstrate analytical, quantitative, problem-solving, and research skills through coursework, projects, or academic work.
• Work effectively in a dynamic, collaborative environment and handle pressure.
• Present findings clearly to non-technical audiences through written and verbal communication.

Preferred Qualifications

• Knowledge of options pricing theory or trading algorithms, or demonstrated finance interest through coursework or experience.
• Knowledge of machine learning and data science concepts, techniques, and tools.
• Confidence and initiative to take ownership and manage projects independently.
• Strong interest in global financial markets and a science or engineering-focused undergraduate background.
• Flexibility, teamwork, excellent attention to detail, and interest in collaborative work.

Application Process

• Submit a complete application including your resume and all relevant application questions.
• Shortlisted candidates receive a coding challenge through HackerRank.
• Candidates then complete a self-recorded video assessment through HireVue.
• Both assessments are required before applications receive further review.
• Apply and complete required elements early because programs close as positions are filled.