Quantitative Systematic Trading Intern

Job Description

About Company

Susquehanna (SIG) is a prominent trading firm known for its quantitative and systematic approach to finance. It fosters a highly competitive and collaborative environment, focusing on leveraging advanced mathematical, statistical, and computational techniques to predict market behavior and execute trading strategies. The company emphasizes a strong educational framework and mentorship for its employees, including interns, ensuring they build foundational knowledge and practical experience from industry leaders. Susquehanna aims to provide a top-tier internship experience, often leading to full-time career opportunities.

Job Description: Quantitative Systematic Trading Intern

This internship offers a hands-on experience mirroring the work of full-time quantitative systematic traders at Susquehanna. It combines a comprehensive educational program with practical project work, allowing interns to build foundational knowledge in quantitative finance and contribute to actual trading strategies.

Key Responsibilities and Activities (What You Can Expect):

• Modelling: Apply advanced quantitative techniques, including probability theory, statistical analysis, and machine learning, to analyze market data and predict market behavior. The primary goal is to generate “alphas” – signals that indicate potential profitable trading opportunities.
• Execution: Develop and refine strategies for executing trading ideas, simulating their performance under competitive market conditions.
• Evaluation: Rigorously backtest developed ideas using extensive historical market data to assess their effectiveness, identify weaknesses, and iterate on strategies for improvement.
• Breadth: Gain exposure to various aspects of quantitative work across different areas and business units within Susquehanna, providing a holistic understanding of the firm’s operations.
• Education: Participate in a structured and comprehensive education program designed to accelerate growth in quantitative finance. Interns will receive personalized mentorship from experienced professionals, ensuring continuous learning and development.
• Collaboration: Work within an open and highly collaborative environment, interacting with multiple teams and gaining exposure to diverse groups and parts of the business.

What Susquehanna is Looking For:

• Education: Candidates must be pursuing a Master’s degree (in their penultimate or final year) in highly quantitative fields such as Mathematics, Physics, Statistics, Electrical Engineering, Computer Science, Operations Research, or Economics.
• Analytical Skills: Possess strong analytical problem-solving abilities, excellent logical reasoning, and a genuine passion for transforming complex data into actionable decisions.
• Communication: Be a clear and effective communicator, able to articulate complex ideas in a fast-paced and highly collaborative team setting.
• Programming Proficiency: Comfortable processing and analyzing large datasets, with strong programming skills in Python. Experience with C++ (or another low-level language) is considered a significant plus.
• Strategic Thinking: Demonstrate a keen interest in strategic games and/or competitive activities, indicating an ability to think strategically and perform under pressure.
• Personal Attributes: Be highly self-motivated, quick to learn, and capable of thriving in a dynamic, fast-moving, and challenging environment.

Application and Logistics:

• Dual Consideration: Applying to this role automatically considers candidates for the Quantitative Research Internship program as well, eliminating the need for separate applications.
• Locations: Internship opportunities are available in Susquehanna’s Philadelphia and New York offices.
• Compensation & Benefits: Master’s quantitative systematic trading interns will receive a competitive weekly base salary of $5700 during the ten-week program. Additionally, interns will receive a signing bonus, housing assistance, daily breakfast and lunch, and various other perks.