Quantitative Research Internship, Master's

About the Role

Quantitative researchers at Susquehanna tackle challenging data and algorithmic problems to inform decision-making in global financial markets. Their work spans statistical inference, optimization, market microstructure, simulation, forecasting, and large-scale data analysis.

As a Quantitative Research Intern, you will gain experience reflecting the work of full-time research teams, participating in a structured education program designed to accelerate your development. Through hands-on research, mentorship, and collaboration with researchers, engineers, and traders, you will explore open-ended questions, design experiments, develop quantitative models, and evaluate ideas through rigorous empirical analysis.

Susquehanna's proprietary datasets and large-scale computing infrastructure, including a rapidly growing cluster of thousands of high-end GPUs, support computationally intensive research, large-scale simulation, and rapid experimentation. Researchers work in small, collaborative teams where ideas are debated openly, evidence guides decisions, and promising research can quickly produce practical impact.

Internship Experience

Your internship begins with Core Curriculum, an immersive program where quantitative and machine learning researchers, traders, and software engineers learn together. You will build a shared foundation in quantitative trading, programming, data analysis, mathematical modeling, and modern machine learning and AI tools before applying these concepts to real-world problems.

You will spend the rest of the summer on the Blast Project—an end-to-end simulation of quantitative work at Susquehanna. You will research and develop a model, turn it into a trading strategy, and run that strategy against high-fidelity historical market data, receiving measurable feedback on its performance daily. This project is designed to expose you to data analysis, modeling, implementation, simulated execution, evaluation, and the judgment calls involved.

You will be paired with a mentor from our research and trading staff, supported by dedicated teaching assistants, and surrounded by a cohort of interns.

What We're Looking For

Intellectually curious researchers who are energized by difficult quantitative problems and motivated by rigorous experimentation.

Requirements & Tech Stack

• A masters' degree in progress, graduating in 2027 or 2028, in computer science, economics, electrical engineering, mathematics, operations research, physics, statistics, or another highly quantitative discipline.
• A strong foundation in probability, statistics, optimization, numerical methods, or quantitative modeling.
• Experience developing research code in Python and working with large datasets.
• Experience with C++, scientific computing, or high-performance computing is a plus.
• Excellent problem-solving skills.
• An interest in using quantitative models to better understand complex systems.
• Strong communication skills.
• An interest in collaborating with researchers, engineers, and traders.
• A track record of intellectual curiosity demonstrated through coursework, independent projects, research, competitions, strategic games, or other analytical pursuits.
• The ability to thrive in an environment that values creativity, evidence-based thinking, and continuous learning.

By applying to this role, you will be automatically considered for the Quantitative Systematic Trading Internship.

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 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.