DataScientist,Advertising

Amazon is investing heavily in building a world-class advertising business, and we are responsible for defining and delivering a collection of advertising tools and products that drive discovery and Advertiser success. Our products are strategically important to our Retail and Marketplace businesses, driving long-term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative, and fun-loving with an entrepreneurial spirit and bias for action. The Marketing Effectiveness & Attribution Science team develops causal inference and machine learning systems to measure the impact of marketing programs across Amazon's advertising ecosystem. We build production-grade attribution models that help business teams understand what's working, optimize resource allocation, and drive advertiser growth. Our work sits at the intersection of econometrics, scalable ML systems, and high-stakes business decisions. As a Data Scientist on this team, you will own end-to-end modeling pipelines — from problem formulation and experimental design to model development, productionization, and stakeholder communication. Major responsibilities include: Translate / Interpret: Partner with cross-functional teams to translate business questions into rigorous causal inference problems Design observational studies and quasi-experiments to measure marketing effectiveness when traditional A/B tests are infeasible Work with data engineering to instrument new data pipelines when existing data cannot answer the causal question Measure / Quantify / Expand: Own and evolve production attribution models across multiple marketing channels Build and maintain causal inference pipelines using methods such as Difference-in-Differences, Synthetic Control, Double Machine Learning, and Media Mix Models Develop scalable PySpark and Python codebases that process large-scale event data Continuously improve mod