Quantitative Research Internship - PhD: Summer 2027
Susquehanna·New York·United States·Hedge Fund & Quant
Susquehanna is hiring a Quantitative Research Internship - PhD: Summer 2027 in New York. Posted 2026-06-29; applications close 2026-08-28 (in 55 days).
Role details
Overview
As a Quantitative Research Intern at Susquehanna, you will work on projects that model the work of our full-time employees. You will also go through a comprehensive education program and interact with mentors who are at the top of their field, allowing you to build foundational knowledge in quantitative finance. You will have the opportunity to build alphas on an actual trading strategy.
What you can expect
- Modelling: Apply probability theory, statistical analysis, and machine learning techniques to predict market behavior and generate alphas
- Execution: Create strategies to execute on modelling ideas under simulated competition
- Evaluation: Backtest ideas using historical market data and revise strategies
- Breadth: Explore all aspects of quant work and different areas of Susquehanna’s business
- Education: Participate in a comprehensive education program and receive personalized mentorship from experienced professionals to accelerate your growth
- Collaboration: Work in an open environment that allows you to collaborate with multiple teams and get exposure to different groups and parts of the business
Susquehanna combines all of the above to provide the best quant internship program in the industry. Join us to see why so many previous quant interns decide to return for a full-time career.
What we’re looking for
- PhDs (in penultimate or final year) in quantitative fields such as mathematics, physics, statistics, electrical engineering, computer science, operations research, or economics
- Analytical problem-solvers with excellent logical reasoning and a passion for turning data into decisions
- Clear communicators in a fast-paced and highly collaborative environment
- Programmers comfortable processing and analyzing large data sets in Python; experience with C++ (or another low-level language) is a plus
- Strategic thinkers with demonstrated interests in strategic games and/or competitive activities
- Self-motivated and quick to learn, thriving in a dynamic, fast-moving environment
By applying to this role, you will be automatically considered for the Quantitative Systematic Trading Internship program. There is no need to apply to both positions to be considered for both.
Opportunities as a quantitative research intern will be available in our Philadelphia and New York offices.
PhD quantitative research interns will receive a $8600 weekly base salary during the ten-week program. In addition, interns will receive a signing bonus, housing, breakfast and lunch, and other perks.
About Susquehanna
Susquehanna is a global quantitative trading firm powered by scientific rigor, curiosity, and innovation. Our culture is intellectually driven and highly collaborative, bringing together researchers, engineers, and traders to design and deploy impactful strategies in our systematic trading environment. To meet the unique challenges of global markets, Susquehanna applies machine learning and advanced quantitative research to vast datasets in order to uncover actionable insights and build effective strategies. By uniting deep market expertise with cutting-edge technology, we excel in solving complex problems and pushing boundaries together.
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Applying to this role
This Quantitative Research Internship - PhD: Summer 2027 role at Susquehanna runs through the firm's own careers portal and expects a CV and cover letter written specifically for the posting, not a portable submission carried across firms. Jorb AI's application agent tailors a CV and cover letter from your background to this posting and tracks the role alongside the rest of your applications.
Jorb AI tracks details for Quantitative Research Internship - PhD: Summer 2027 at Susquehanna. Postings refresh hourly from primary careers pages. Job details mirror the firm's posting; the apply link goes directly to the source. Last refreshed 2026-07-03.
