Quantic - Quantitative Researcher Intern (Summer 2027)

Walleye Capital Internships•Boston, MA

23h•$20,000•Onsite

About The Position

Walleye Capital is seeking highly quantitative and creative Quantitative Researcher Interns to work in the rapidly growing Quantic team based out of Boston. Quantic is Walleye’s principal quantitative investment business, established in 2016 as one of its core investment strategies. Quantic has subsequently evolved into one of the most successful trading teams in the industry. We are a tight-knit, collaborative, and intellectually rigorous group of scientists, engineers, and traders leveraging advanced statistical modeling techniques to identify and capitalize on profitable trading opportunities in global equities, options, and futures. Our pragmatic, engineering-driven culture, where achieving goals—and achieving them the right way—takes precedence, fosters collaboration among colleagues, confident that the best ideas arise through cross-disciplinary exchange. Our commitment to continuous self-reflection and growth drives us to build the strongest possible platform for our team's future success. We are seeking talented researchers to help elevate our capabilities and join us on this journey. This role offers the opportunity to engage directly with cutting-edge data analysis, portfolio optimization, platform development, and operation of fully automated trading systems. You will join a team where your creativity, initiative, and teamwork will make direct impacts on trading profits for our investors. We invite researchers with a proven record of innovation and achievement in their fields to apply. As a Quantic Intern, you’ll work directly with experienced team members on meaningful projects that impact trading strategies and operations. You’ll have the opportunity to work on high-impact initiatives and develop your skills in a dynamic setting where innovation, teamwork, and talent drive success. We are seeking students with strong technical backgrounds (e.g., mathematics, statistics, computer science, or engineering), demonstrated initiative, and an interest in quantitative trading and research. Successful interns are curious, collaborative, and eager to tackle complex problems in a fast-paced, supportive environment. The internship is 10 weeks in length and will take place in Boston from June to August 2027.

Requirements

Pursuing an undergraduate or advanced degree in computer science, engineering, statistics, mathematics, or a related field, with an expected graduation date between December 2027 and June 2028.
Possess strong programming skills—particularly in Python or R—and hold experience working with large datasets, APIs, or databases.
Demonstrate rigorous analytical thinking, statistical modeling abilities, and familiarity with techniques from machine learning, optimization, or time-series analysis.
Self-starters who enjoy digging into complex, open-ended problems and can work both independently and collaboratively with a team.
Genuine interest in financial markets, systematic investing, AI/LLM application, and using technology in dynamic, data-rich environments.
Showcase creativity and enthusiasm for leveraging AI tools to enhance productivity, improve processes, and generate investment alpha.
Thrive in a collaborative culture that values intellectual humility, creativity, and continuous learning.

Responsibilities

Research, design, and test predictive signals, data sets, and systematic trading strategies.
Extract and analyze large datasets from structured and unstructured sources, applying advanced statistical and computational methods.
Enhance research infrastructure and tools for trading, risk management and attribution.
Develop machine learning models to predict patterns in asset returns, risks, trading costs, or other portfolio-relevant variables.
Design and implement scalable code across various stages of the investment process.
Work in Python and/or R, with opportunities to contribute to research tools and libraries.
Leverage AI tools including LLM-based analytical pipelines to enhance processes and analyses.