Responsibilities

• Build and improve multi-touch attribution models using causal ML and statistical approaches (e.g., Shapley values, Double Machine Learning, Neural Networks)
• Research and apply quasi-experimental techniques (e.g., difference-in-differences, synthetic controls, instrumental variables) to quantify marketing effectiveness
• Develop internal Python packages and tooling to support attribution pipelines, causal model development, and test evaluation
• Scale modeling workflows using Google Cloud tools (e.g., BigQuery, Vertex AI, Cloud Functions, microservices)
• Partner with stakeholders in marketing, product, and operations to define success metrics and shape test designs
• Guide experimentation and lift study strategy for large marketing investments across channels like Paid Search, Social, Display, and TV

Requirements

• Ph.D. with 0-1+ years of experience, or 2+ years of industry experience with a Master’s degree in a quantitative field (e.g., Statistics, Economics, Operations Research, Computer Science)
• Strong foundation in causal inference, experimental design, and statistical learning
• Fluency in Python for model development, data analysis, and package building (R a plus)
• Familiarity with modern data infrastructure (e.g., SQL, BigQuery, Airflow) and reproducible research workflows
• Strong written and verbal communication skills, with the ability to explain technical methods to non-technical stakeholders