Research Assistant for Big Data Analytics/Marketing Research – Tepper School of Business

Carnegie Mellon University•Pittsburgh, PA

1d•Hybrid

About The Position

The Tepper School of Business, part of Carnegie Mellon University, is seeking a Research Assistant to support an academic research project involving large-scale data analysis. This role will collaborate with Professors Mohsen Foroughifar, Joy Lu, and Minkyung Kim, focusing on data processing, statistical/econometric modeling, and research workflow support. The Tepper School emphasizes the intersection of business, technology, and human creativity, empowering individuals to use artificial intelligence (AI) and other advanced tools through experimental, hands-on learning, while also building ethical and equitable leadership skills. The university values adaptability, excellence, and passion, seeking a team member who can effectively interact with a varied population of internal and external partners at a high level of integrity and support the mission of the university through their work.

Requirements

Strong Python skills for data analysis (e.g., pandas/numpy/statsmodels or equivalent).
Familiarity with econometrics and statistical analysis (e.g., regression, causal inference basics, panel data, clustering/robust SEs—depending on project needs).
Experience working with large datasets (performance-aware data handling, efficient workflows).
Strong attention to detail, ability to write clean and reproducible code, and ability to communicate results clearly.
Bachelor’s degree in a field closely related to Marketing, Information Systems, Statistics, Economics, Business Analytics, Computer Science, or a related discipline.
3-5 years prior research experience (academic or industry research setting).
Experience with version control (Git/GitHub) and reproducible research practices.
Successful completion of a pre-employment background check

Nice To Haves

Master’s degree preferred
Experience with big-data tooling (e.g., SQL, Spark/Databricks, cloud computing) is a plus

Responsibilities

Clean, merge, and process large-scale datasets; develop reproducible data pipelines.
Conduct statistical analyses and econometric modeling in support of research hypotheses.
Implement and document code in Python (preferred), including data manipulation and estimation routines.
Produce tables, figures, and written summaries of results for internal research use.
Support robustness checks, validation analyses, and sensitivity analyses.
Maintain clear documentation, version control practices, and organized project files.
Collaborate closely with faculty supervisors; participate in regular check-ins and respond to iterative analysis requests.
Other duties as assigned