About the role#
The PhD quantitative research internship is a 10-week, in-person program designed to provide experience with real-world financial data and research challenges. The program begins with two weeks of training covering research processes, machine learning, statistics, market mechanics, Python, and internal infrastructure. Following training, you will be matched with a trading team to work 1:1 with experienced researchers on a project tied to live business needs. This role is a primary pathway to full-time employment.
What you'll do#
- Own a research project end-to-end, including predictive modeling, alpha research, and model optimization.
- Collect, clean, and explore large, complex datasets to engineer predictive signals.
- Build, fit, and evaluate models using a supercomputing grid.
- Present research results to your team throughout the summer, culminating in a final presentation.
- Receive daily 1:1 mentorship from experienced quantitative researchers.
What you'll need#
- Currently pursuing a PhD in Statistics, Mathematics, Computer Science, Physics, Electrical Engineering, Operations Management, Economics, or a highly quantitative field.
- Experience conducting an in-depth research project using real-world data.
- Programming proficiency in Python, including the ability to read, understand, and debug code.
- Ability to form hypotheses, design rigorous tests, and draw statistically sound conclusions.
- Ownership of your research, with the ability to justify your methodology and choices.
- Creativity and initiative to explore ideas independently.
- Perseverance in tackling difficult research problems.
- Proficiency in C++ is considered a plus.
- Familiarity with financial markets is helpful but not required; training will be provided.
Location & details#
- Locations: Chicago, Illinois and New York, New York.
- Term: Summer 2027.
- Modality: On-site.
- Status: Full-time, paid internship.
- Sponsorship: Available for qualified candidates.
