Student | Data Science Intern — Mendoza Research Team

The University of Notre Dame is a vibrant, mission-driven community where every employee is valued and supported. Rooted in a tradition of excellence and inspired by our Catholic character, Notre Dame is committed to fostering an environment of care that nurtures the whole person—mind, body, and spirit.

About the Role

The Mendoza Research Team provides the technical and quantitative backbone for faculty research across Accountancy, Finance, IT Analytics & Operations (ITAO), Marketing, and Management & Organization (MNO). As a Research Data Science Intern, you will work directly alongside our team of full-time data scientists to support active, high-impact faculty research projects. This is a hands-on, highly collaborative role designed to offer direct mentorship, build your applied technical toolkit, and give you real-world experience in data pipelines and statistical modeling.

Responsibilities

• Data Engineering & Preparation: Clean, merge, and validate complex research datasets, including financial/market data, administrative records, survey results, and web-scraped data.
• Statistical Modeling: Assist in building, testing, and refining statistical and machine learning models.
• Code Optimization & Reproducibility: Write clean, reproducible code using standard data science languages (primarily Python and R).
• Data Visualization: Design intuitive charts, plots, and summary tables to communicate findings effectively.
• Documentation & Version Control: Maintain clear documentation, data dictionaries, and organized code repositories using Git workflows.
• Faculty Collaboration: Participate in team check-ins and contribute to technical discussions with faculty researchers.

Requirements

• Currently enrolled Higher Education student in good academic standing.
• Proficiency in Python or R for data manipulation and analysis.
• Familiarity with data cleaning, basic statistical methods, and visualization tools.
• Strong attention to detail and an interest in academic or quantitative research.
• Experience with Git/GitHub, SQL, or web scraping is a plus, but not required.

Qualifications

• Currently enrolled undergraduate student, any major.
• Coursework in statistics, econometrics, computer science, or analytics.
• Working proficiency in Python or R.
• Comfortable with (or eager to learn) tools like SAS, SQL, and Git.
• Strong attention to detail and ability to follow data handling/confidentiality protocols.
• Available for 15-20 hours/week.

Preferred:

• Coursework in machine learning, causal inference, or experimental design.
• Experience with large or messy real-world datasets.
• Prior research assistant experience.

Pay

Starting at $15/hour.

Schedule

• 15-20 hours/week during the academic term.
• Schedule will be structured around your academic requirements.

Duration: Starting Fall 2026.