Design and build models that quantify causal impact, optimize decision‑making, and drive value for users, advertisers, and the business
Develop and productionize causal machine learning solutions (e.g., uplift modeling, heterogeneous treatment effect estimation) using observational and experimental data
Design, analyze, and interpret A/B tests and quasi‑experiments; collaborate closely with product and engineering partners to shape experimentation strategies
Evaluate technical tradeoffs between model complexity, bias/variance, scalability, and interpretability
Conduct code reviews, maintain high engineering standards, and build scalable, maintainable infrastructure
Contribute to rapid iteration cycles while ensuring methodological rigor
Requirements
Bachelor’s degree in computer science, statistics, economics, or a related technical field, or equivalent practical experience
5+ years of post‑Bachelor’s experience in machine learning, with hands‑on experience in causal inference or experimentation; or Master’s degree in a technical field + 4+ years of post‑grad machine learning experience; or PhD in a relevant technical field + 2 years of post‑grad machine learning experience
Demonstrated experience building models to support product decision‑making and policy evaluation through causal techniques
Experience designing and analyzing online experiments (A/B tests) and leveraging causal ML in production systems