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.

We seek individuals who:

Are pursuing a PhD degree in computer science, engineering, statistics, operations research, 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.

Are self-starters who enjoy digging into complex, open-ended problems and can work both independently and collaboratively with a team.

Quantic - PhD Quantitative Researcher Intern (Summer 2027)

Responsibilities

Company

Walleyecapital External Students

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