2027 Quantitative Research – Asset Management - Off-Cycle - London

2027 Quantitative Research – Asset Management - Off-Cycle - London

London Full-Time No working from home possible
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At a Glance

  • Tasks: Apply quantitative methods to real-world investment challenges and collaborate with top professionals.
  • Company: Join JPMorgan Chase, a leader in asset management with a supportive culture.
  • Benefits: Gain hands-on experience, build a network, and potential full-time offers after the internship.
  • Other info: Embrace diversity and innovation in a fast-paced, collaborative setting.
  • Why this job: Make an impact in finance while developing your skills in a dynamic environment.
  • Qualifications: Pursuing a degree in relevant fields with proficiency in Python, C++, or Java.
Description

Job Description

At JPMorganChase, we champion your innovative ideas through a supportive culture that helps you every step of the way as you build your career. If you are passionate, curious and ready to make an impact, we are looking for you.

Job Summary:

As a Quantitative Research – Asset Management Off-Cycle Intern in the Asset Management Product Program, you will sit at the intersection of investment science and technology—working directly with portfolio managers and research teams who oversee trillions in client assets. You'll apply academic knowledge to real-world portfolio construction, risk, and alpha-generation challenges, gain hands-on experience with institutional-scale datasets, and build a valuable network across one of the world's largest asset managers. This program sets a solid foundation for your career, with potential full-time offers upon successful completion.

Job Responsibilities:

  • Apply quantitative investing and data science methods—such as factor modeling, optimization, and machine learning—to research problems across asset classes and datasets.
  • Analyze structured and alternative data to identify patterns, return drivers, and portfolio construction insights.
  • Partner with portfolio managers, traders, and other investment professionals to translate research into actionable investment strategies and client solutions.
  • Design robust backtests and validation frameworks; assess strategy performance, stability, and risk implications at the portfolio level.
  • Implement research in production-quality code; maintain and enhance research infrastructure and investment/trading tools.
  • Contribute to solutions that serve institutional, wealth, corporate, government, not-for-profit, and individual clients worldwide.
  • Develop, validate, and enhance mathematical models and algorithms used in portfolio management and asset allocation.

Required Qualifications, Capabilities, and Skills:

  • Enrolled in a Bachelor's or Master's degree in mathematics, statistics, physics, engineering, computer science, economics, finance, or data science/machine learning, graduating between September 2026 and March 2028.
  • Proficiency in Python, C++, or Java.
  • Strong analytical, quantitative, and problem-solving skills.
  • Excellent communication skills for presenting complex concepts to both technical and non-technical audiences.
  • Interest in investing, portfolio analytics, global markets, and quantitative research.
  • Ability to thrive in a fast-paced, collaborative environment.

Preferred qualifications, capabilities and skills

  • Genuine interest in financial markets, investing, portfolio construction, and macro-level economics.
  • Coursework or project experience in time-series analysis, optimization, or statistical learning.
  • Experience with R, MATLAB, or SQL.
  • Familiarity with data visualization tools like Tableau or Power BI.
  • Understanding of asset management products (mutual funds, ETFs, separately managed accounts), financial instruments, and market dynamics.
  • Strong organizational skills for managing multiple projects.
  • Ability to articulate complex quantitative concepts to diverse audiences.

About you

We are looking for innovative problem-solvers with a passion for developing complex solutions that support our global business.

Beyond that, what we’re most interested in are the things that make you unique: the personal qualities, outside interests and achievements beyond academia that demonstrate the kind of person you are and the difference you could bring to the team.

Join us

At JPMorganChase, we’re creating positive change for the diverse communities we serve. We do this by championing your innovative ideas through a supportive culture that helps you every step of the way as you build your career. If you are passionate, curious and ready to make an impact, we are looking for you.

What’s next?

We will review applications as they are received and extend offers on a rolling basis. We strongly encourage you to apply early, as programs will close as positions are filled.

JPMorganChase is committed to creating an inclusive work environment that respects all people for their unique skills, backgrounds and professional experiences. We strive to hire qualified, diverse candidates, and we will provide reasonable accommodations for known disabilities.

Visit jpmorganchase.com/careers for upcoming events, career advice, our locations and more.

©2025 JPMorgan Chase & Co. JPMorganChase is an equal opportunity and affirmative action employer Disability/Veteran

2027 Quantitative Research – Asset Management - Off-Cycle - London employer: JPMorganChase

J.P. Morgan Europe Limited is an exceptional employer, offering a dynamic work culture that fosters collaboration and innovation in the heart of the financial sector. Employees benefit from comprehensive growth opportunities, competitive compensation, and a commitment to professional development, all while contributing to impactful consumer banking initiatives. Working here means being part of a prestigious institution that values insights and empowers its team members to drive meaningful change.

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Contact Details:

JPMorganChase Recruitment Team

We think you need these skills to ace 2027 Quantitative Research – Asset Management - Off-Cycle - London

Quantitative Investing
Data Science Methods
Factor Modeling
Optimization
Machine Learning
Data Analysis
Python