AI & Quantitative Finance Research Intern — Part-Time, Remote

Job No: 512233
Division/Organization: Community Partners
Department: Ivy Online, Inc.
Job Type: Local Work Study Student Jobs
Remote Eligbility: All Remote
Location: MEMORIAL LIBRARY
Salary/Wage Range or Lump Sum: $20.00
Job Categories: Critical Thinking/Problem Solving, Digital Technology, Teamwork/Collaboration, Information Technology and Computers, STEM, Data Analysis, Graduate Assistant-Research, Graduate Assistant-Programs/Projects, Career Management, Federal Work-Study Preferred

Ivy Online Inc. is an ed-tech and applied AI research enterprise expanding its focus into Fin Tech, LLM Evaluation, and Quantitative Financial Modeling. We lead agile, publication-oriented research projects examining counterfactual LLM reliability, econometric time-series models, and market analytics. Our research initiatives aim to submit high-impact findings to top-tier AI and financial technology venues (such as AAAI, NeurIPS, and Fin Tech conferences).

May 2027

All Remote

Minimum: 10 Maximum:15

Weekly Total: 10 - 15 hours / week
Schedule Pattern:
Flexible / Self-paced
Meeting Availability:
Mon-Fri, flexible evening or weekend check-ins available.
Exam Policy:
Reduced hours or temporary pause allowed during midterms and finals.

Minimum: $20.00

3

Qualifications & Skills:

• 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, Num Py, 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.

Federal Work-Study:

We are a UW-Madison Federal Work-Study partner. Students with work-study eligibility are encouraged to apply. Applicants without work-study funding will also be considered, including master’s and PhD students.

Required Qualifications:

• Working proficiency in Python, including data manipulation and analysis using pandas and Num Py.
• A foundational understanding of probability and statistics, including hypothesis testing, confidence intervals, and regression.
• Familiarity with basic machine learning concepts and evaluation practices, including train/validation/test splits, overfitting, and data leakage.
• Ability to independently debug code, check results, document procedures, and clearly explain findings.
• Relevant coursework, research experience, or substantial project experience.

Preferred Qualifications:

Experience in one or more of the following areas is a plus:

• Time-series analysis and forecasting, including lagged features, rolling-window evaluation, and chronological data splitting.
• Financial data analysis, quantitative finance, factor research, portfolio modeling, or backtesting.
• Large language models, including API-based experiments, prompt design, and model evaluation.
• Statistical methods for comparing models, such as paired comparisons and bootstrap confidence intervals.
• Research reproducibility tools and practices, including Git, experiment tracking, and clear technical documentation.

Applicants are not expected to have experience in all of these areas. Strong coursework or independent projects demonstrating relevant skills are welcome. Prior research experience is a plus; publications are not required.

Research and Publication Opportunities:

Interns will have opportunities to…