Remote AI & Quant Finance Research Intern (Part-Time)

University of Wisconsin–Madison; Safford (AZ); Remote; USD 23,000 - 32,000; Part time

Job summary

University of Wisconsin–Madison seeks a part-time AI & Quant Research Intern for remote work with flexible hours (10–15 per week). The role focuses on experimental design, data analysis, and academic writing in FinTech and AI research. Students may contribute to conference submissions and coauthorship based on meaningful contributions.

The position supports UW‑Madison’s Federal Work‑Study program; eligibility encouraged, with consideration for non-work-study applicants as well.

Qualifications

• Currently enrolled student at UW‑Madison in Computer Sciences, Data Science, Quantitative Finance, Statistics, Financial Engineering, or a related quantitative field.
• Strong Python programming skills (PyTorch, Pandas, NumPy, Scikit-learn).
• Familiarity with financial time-series analysis, econometric models, machine learning or LLM prompt engineering is highly desirable.
• Self-motivated, resourceful in troubleshooting code, and disciplined with modular Git-based workflows.

Responsibilities

• Interns will contribute to experimental design, analysis, and academic writing.
• Projects may lead to submissions to AI and related research conferences or workshops.
• Coauthorship will be based on substantive contributions and the venue’s authorship requirements.
• Publication, acceptance, and authorship are not guaranteed.

Skills

Python

PyTorch

Pandas

NumPy

Scikit-learn

Education

UW‑Madison student in CS/DS/Quant Finance/Statistics/Financial Engineering

Tools

Git

Job description

University of Wisconsin–Madison seeks a part-time AI & Quant Research Intern for remote work with flexible hours (10–15 per week). The role focuses on experimental design, data analysis, and academic writing in FinTech and AI research. Students may contribute to conference submissions and coauthorship based on meaningful contributions.

The position supports UW‑Madison’s Federal Work‑Study program; eligibility encouraged, with consideration for non-work-study applicants as well.