2027 AI Quant Summer Analyst

StepStone Group•La Jolla, CA

About The Position

StepStone’s AI & Quantitative Investment Research team applies artificial intelligence, quantitative methods, and proprietary data to investment research across private markets. The team works at the intersection of investing, data, and technology to identify investment signals, strengthen portfolio insights, and turn institutional knowledge into practical decision-support tools. Its research spans the full lifecycle, from framing investment questions and evaluating data to testing analytical approaches and communicating findings, with an emphasis on rigorous, explainable insights that inform investment judgment. StepStone is seeking an analytical and technically capable intern to support applied AI and quantitative research. The position offers hands-on exposure to investment questions, proprietary datasets, and collaboration with investment, data, and technology professionals. The intern will help advance research from an initial question through analysis and interpretation, while developing practical experience with analytical tools and learning private-markets investing fundamentals. The role is intended for collaborative, intellectually curious candidates who take ownership, question assumptions, respond to feedback, and work through ambiguous problems. The internship is based in StepStone’s La Jolla office during the 2027 summer intern program. The posting also expresses a preference for the candidate to work part-time remotely during the 2028 fall semester. The application deadline is November 27, 2026. Compensation is listed as $30 per hour; actual pay may vary based on job-related factors, and the estimate excludes benefits and other possible compensation.

Requirements

Pursuing a bachelor’s or master’s degree in Mathematics, Statistics, Econometrics, Computer Science, Engineering, Data Science, Finance, Economics, or a related quantitative field.
Expected to graduate in Winter 2027 or Spring 2028.
Strong Python coding skills, including experience with data pipelines and quantitative modeling.
SQL proficiency.
Demonstrated ability to work with large, messy real-world datasets.
Strong problem-solving skills and intellectual curiosity, including willingness to challenge assumptions, investigate unexpected results, and understand the reasoning behind an analysis.
Clear written and verbal communication skills, including ability to explain technical concepts and analytical findings to investment stakeholders.
Openness to feedback and continuous improvement.
Willingness to contribute at all levels, including complex modeling and data preparation.
Candidates must be at least 18 years old to apply.

Nice To Haves

Advanced coursework or research experience in quantitative methods, machine learning, or empirical analysis.
Experience or coursework in AI/ML, including LLMs, NLP, data retrieval, or vector embeddings.
Familiarity with cloud data platforms such as Snowflake or Azure, vector databases, or workflow orchestration tools such as Airflow.
Ability to work in person in the La Jolla office during the 2027 summer intern program and part-time remotely during the 2028 fall semester.

Responsibilities

Support applied AI and quantitative research using StepStone’s proprietary data and institutional knowledge to identify investment signals, performance drivers, portfolio trends, and emerging risks across private markets.
Apply statistical, econometric, machine-learning, and natural-language-processing methods to structured and unstructured information, producing robust, explainable, and economically intuitive findings.
Work with investment, data, and technology stakeholders to translate investment questions into analytical approaches and connect findings to investment selection, portfolio construction, monitoring, benchmarking, and risk assessment.
Document research approaches, assumptions, findings, and limitations.
Communicate conclusions clearly to technical and non-technical audiences, including investment stakeholders.
Contribute to collaborative projects that may span multiple asset classes and support investment judgment.