Key Responsibilities Data Engineering & Preparation:

Clean, merge, and validate complex research datasets, including financial/market data, administrative records, survey results, and web-scraped data.\n

Statistical Modeling:

Assist in building, testing, and refining statistical and machine learning models.\n

Code Optimization & Reproducibility:

Write clean, reproducible code using standard data science languages (primarily Python and R).\n

Data Visualization:

Design intuitive charts, plots, and summary tables to communicate findings effectively.\n

Documentation & Version Control:

Maintain clear documentation, data dictionaries, and organized code repositories using Git workflows.\n

Faculty Collaboration:

Participate in team check-ins and contribute to technical discussions with faculty researchers.

What We Are Looking For:

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

Qualifications Required:

Currently enrolled undergraduate student, any major\nCoursework in statistics, econometrics, computer science, or analytics\nWorking proficiency in Python or R\nComfortable with (or eager to learn) tools like SAS, SQL, and Git\nStrong attention to detail and ability to follow data handling/confidentiality protocols\nAvailable for 15-20 hours/week

Preferred:

Coursework in machine learning, causal inference, or experimental design\nExperience with large or messy real-world datasets\nPrior research assistant experience How to

Apply:

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Student | Data Science Intern — Mendoza Research Team