2027 Quantitative Researcher Intern, London

2027 Quantitative Research Intern - London

Millennium is a global, diversified alternative investment firm, founded in 1989. Defined by evolution, innovation and focus, Millennium’s mission is to deliver results for our investors.

Our people are empowered with both independence and support: the autonomy to pursue ideas with conviction and the backing of a global network committed to collaboration, disciplined risk management and continuous learning. With opportunities to deepen expertise and accelerate development, talent at Millennium is equipped to adapt, evolve and build lasting impact over time. Discover how transformative growth accelerates impact.

June 2027

11 weeks

Millennium’s teams operate in a dynamic and entrepreneurial environment, supported by the firm’s resources, technology and strong commitment to innovation across data and markets. Within areas such as Equities, Fixed Income, Credit, Commodities, Technology, Execution Services and Portfolio Pricing and Valuations, teams combine deep specialization with a rigorous risk framework and a collaborative culture focused on delivering high-quality results.

Explore and analyze a wide range of datasets, including market and alternative data, using statistical, machine learning, econometric, and applied mathematics techniques.

Backtest strategies to evaluate performance, robustness, and practical application.

Build and enhance research tools, including data acquisition and normalization libraries, backtesting tools, and portfolio optimization capabilities.

Partner closely with quantitative researchers and investment professionals to support live investment strategies.

Apply analytical and programming skills to solve real-world quantitative research challenges.

Graduating between December 2027 and July 2028.

Academic Excellence: On track to graduate with a 2:1 or 1st class degree

Pursuing a Bachelor’s or Master’s degree in a technical or quantitative discipline such as financial engineering, quantitative and computational finance, statistics, applied mathematics, physics, computer engineering, computer science, operations research, data science, or engineering.

Demonstrated proficiency in Python; experience with additional languages such as C++ is a plus.

Ability to apply AI tools effectively in quantitative workflows and clearly explain methodology, rationale, and output validation.

Understanding of data structures and algorithms.

Experience conducting an in-depth research project using real-world data.

Solid knowledge of statistical and machine learning techniques.

Availability to commit fully to the internship for the full program duration.