Quantitative Investment Internship Program - Summer 2027

Acadian Asset Management LLC•Boston, MA

1d•$50 - $65•Hybrid

About The Position

Acadian Asset Management is a global, systematic investment manager founded in 1986, specializing in data-driven investing. With headquarters in Boston and offices in Singapore, London, and Sydney, the firm manages over $232 billion for institutional clients worldwide. Acadian utilizes advanced technology, extensive datasets, and multidisciplinary expertise to navigate complex markets and uncover unique insights. The company culture is collaborative and intellectually curious, welcoming diverse perspectives and fostering innovation. They are united by a shared purpose of delivering effective client outcomes and supporting each other in challenging yet rewarding work. Acadian offers a flexible hybrid work environment, comprehensive benefits, and a casual yet focused office culture.

Requirements

Currently enrolled in a Master’s or PhD program in a quantitative discipline (e.g., statistics, mathematics, economics, financial engineering, computer science, physics, operations research).
Expected degree completion between December 2027 and June 2028.
Ability to write code to complete work, primarily in Python.
Interest in applying quantitative methods to financial markets.
Experience carrying an empirical or computational project from an open question through to a finished result, using real data and own code (e.g., thesis, dissertation, published paper, research assistantship, professional project).
Availability for the full 10-week program (June-August 2027).
Expected to work from the Boston office at least four days per week.

Responsibilities

Join an investment team and own one substantial project from framing through delivery.
Define an answerable question for projects that usually begin as loosely framed needs.
Adapt project approach as evidence develops and incorporate input from colleagues.
Use coding and AI tools thoughtfully and validate produced work.
Present work to investment teams at the end of the summer.
Find and test factors that predict returns and risk, developing systematic signals and working with new datasets.
Understand how risk models behave by examining their structure and assumptions.
Turn investment models into live portfolios, focusing on portfolio construction, trading, costs, and strategy implementation across client accounts.
Build tools for investment teams, including internal tools and data pipelines that utilize firm data and generative AI.
Modernize systems supporting the investment process, including rewriting production code and improving the speed and repeatability of research and model updates.