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Programme
Internship
Location
Hong Kong
About the role
Quantitative Research Interns at Susquehanna model market behavior, develop trading strategies, backtest ideas, and build alphas using probability, statistical analysis, and machine-learning techniques. The Summer 2027 internship is U.S.-based, with full-time placement in Hong Kong upon graduation. Interns receive structured education, mentorship, and exposure to collaborative quantitative trading teams.
Responsibilities
- Apply probability theory, statistical analysis, and machine learning to predict market behavior and generate alphas.
- Create strategies to execute modelling ideas under simulated competition.
- Backtest ideas using historical market data and revise strategies.
- Explore quantitative work across different areas of Susquehanna’s business.
- Collaborate with multiple teams and gain exposure to different business groups.
Training and Mentorship
- Participate in a comprehensive education program focused on foundational quantitative finance knowledge.
- Receive personalized mentorship from experienced professionals throughout the internship.
Program Details
- The internship is scheduled for Summer 2027 and is U.S.-based.
- Successful interns receive full-time placement in Susquehanna’s Hong Kong office upon graduation.
- Visa sponsorship is available for this position.
- Applicants are automatically considered for the Quantitative Systematic Trading Internship program.
Requirements
- Master’s student in a quantitative field, currently in the penultimate or final year.
- Relevant fields include mathematics, physics, statistics, electrical engineering, computer science, operations research, or economics.
- Strong analytical problem-solving skills, logical reasoning, and interest in turning data into decisions.
- Clear communication skills and ability to work collaboratively in a fast-paced environment.
- Comfort processing and analysing large datasets using Python.
- Self-motivated, quick to learn, and interested in strategic games or competitive activities.
Preferred Qualifications
- Experience with C++ or another low-level programming language is advantageous.