Supplemental Information
The ideal candidate for this position has a background in Marketing, Economics or Computer Science, and a strong interest in applying econometric and quantitative methods to the study of marketing problems, including but not limited to advertising, digital platforms, demand and supply estimation, and consumer behavior.
A successful candidate has:
- Strong familiarity with causal inference, including both experimental and non-experimental econometric methods
- Demonstrated interest in Industrial Organization and/or Digital Economics
- Strong background in regression analysis and applied econometrics
- Experience working with large-scale panel and cross-sectional datasets
- Experience with text parsing, natural language processing (NLP), and machine learning techniques
- Programming proficiency in R, Python, and Stata
- Familiarity with working with unstructured data and APIs
- Knowledge of additional programming languages (e.g., SQL) is a plus
- Experience in designing, implementing, or analyzing experiments on digital advertising platforms (e.g., Meta, Google, TikTok, etc.) is a plus