Quantitative Trader Intern (Summer 2026) at Tower… · New York

Summary: Tower Research Capital is a leading quantitative trading firm known for its high-performance platform and independent trading teams. The Quantitative Trader Intern will be responsible for designing and implementing high-frequency trading algorithms, analyzing market data for trading opportunities, and developing tools for data analysis. Responsibilities: - Designing, implementing, and deploying high-frequency trading algorithms - Exploring trading ideas by analyzing market data and market microstructure for patterns - Creating tools to analyze data for patterns - Contributing to libraries of analytical computations to support market data analysis and trading - Developing, augmenting, and calibrating exchange simulators Required Qualifications: - Bachelor's, Master's, or PhD student - Majoring in computer science, mathematics, physics, electrical engineering, or related fields - Proficient in an object-oriented programming language (C++ and Python preferred) - A working knowledge of Linux/Unix - Strong problem-solving abilities - A passion for new technologies and ideas - The ability to manage multiple tasks in a fast-paced environment - Strong communication skills - Interest in financial markets Preferred Qualifications: - Past industry experience - Experience as a Teaching Assistant and/or participation in relevant Olympiads - Familiarity with machine learning, data analysis, market research and data modeling Required Skills: C++, Python Important Skills: Linux/Unix, Machine Learning, Data Analysis Nice-to-Have Skills: Interest in Financial Markets, Problem-Solving, Communication Skills Internship Start Date: Start in 2026 Summer Benefits: Competitive compensation package, Housing accommodation, Free breakfast, lunch, and snacks daily, Exciting networking and social events (e.g. Broadway shows, escape rooms, cooking classes), Opportunities to learn from senior management across the firm, Mentors from your alma mater and other top institutions