2026 QD Execution Services Sophomore Summer Internship

Schonfeld•New York, NY

3d•$50,000 - $55,000

About The Position

The Execution Services team is seeking a quantitative-development summer intern dedicated to improving execution with large-language-model (LLM) technology. You will design prototype analytics systems, data pipelines, and human-in-the-loop tools that: Improve information access to execution policies, procedures, and desk knowledge Accelerate post-trade analysis and execution-quality reviews The ideal candidate has a solid foundation in mathematics, computer science, and machine learning; demonstrates research-level rigor; and has hands-on experience building LLM-based tools. Our Internship Kick-start your career in finance with the Schonfeld Summer Internship! Over the course of 10 action-packed weeks, you will have the opportunity to build solutions that tackle real-world challenges and leave a lasting impact on the firm. As an intern, you’ll join a close-knit team that is ready to support your growth and accelerate your career. We will pair you with a dedicated manager and a mentor who’ll guide your development over the course of the internship and beyond. Throughout the program, you will have the chance to connect with senior leaders through a series of learning sessions, attend hands-on skills workshops, network at social events and get a behind-the-scenes look at how various functions power the firm. We’ll empower you to bring your ideas to life and make an impact. Who We Are Schonfeld is a global multi-manager hedge fund that strives to deliver industry-leading risk-adjusted returns for our investors. We leverage both internal and external portfolio manager teams around the world, seeking to capitalize on inefficiencies and opportunities within the markets. We draw from decades of experience and a significant investment in proprietary technology, infrastructure and risk analytics to invest across four main strategies: Quant, Tactical, Fundamental Equity and Discretionary Macro & Fixed Income. Our Culture At Schonfeld, we’ll invest in you. Attracting and retaining top talent is at the heart of what we do, because we believe that exceptional outcomes begin with exceptional people. We foster a culture where talent is empowered to continually learn, innovate and pursue ambitious goals. We are teamwork-oriented, collaborative and encourage ideas—at all levels—to be shared. As an organization committed to investing in our people, we provide learning and educational offerings and opportunities to make an impact. We encourage community through internal networks, external partnerships and service initiatives that promote inclusion and purpose beyond the firm’s walls.

Requirements

Rising junior pursuing a B.S. in Computer Science, Mathematics, Engineering or related field.
Solid Python skills (Pandas, NumPy).
Hands-on experience with LLM APIs (OpenAI, Anthropic, etc.).
Familiarity with AI-assisted coding tools (e.g., GitHub Copilot, Claude Code).
Comfortable working with high-dimensional data; basic data-analysis background.
Independent, research-oriented mindset with strong written documentation skills.

Nice To Haves

KDBQ development experience.

Responsibilities

Build GenAI systems over execution-related documents, procedures, and guidelines.
Design embedding and vector-search pipelines to support accurate, context-aware Q&A.
Ensure outputs are grounded, traceable, and explainable, with clear links to source material.
Conduct evaluations to monitor and track retrieval accuracy, precision, and overall reliability.
Develop GenAI tools to summarize and analyze execution reports and post-trade data.
Help build workflows that enable users to (a) review execution outcomes more efficiently and (b) identify anomalies or patterns for human investigation.
Apply statistical and numerical reasoning to assess output consistency and robustness.
Generate clear, high-quality documentation to support best-execution decisions.
Translate structured system outputs into well-formed, human-readable explanations.
Help standardize documentation templates and improve clarity and consistency.