Drive the future of marketing measurement at Amazon by leading our experimentation and causal methods standards across a multi-billion dollar global marketing portfolio. In this role, you'll shape how Amazon evaluates and optimizes its marketing investments through innovative experimental design, rigorous causal analysis, and innovative applied Bayesian methods at scale to support in risk based marketing spend decision making. This role combines deep technical expertise with strategic impact, working at the intersection of advanced economic and statistical methods and billion-dollar business decisions.
The ideal candidate brings deep expertise in causal inference, experimental design, statistics, and Bayesian methods, with a passion for translating complex methodologies into actionable business insights. You'll have the opportunity to significantly impact how Amazon makes multi-billion dollar marketing decisions while advancing the field of marketing measurement science.
Join us in building the next generation of marketing measurement capabilities at Amazon.
Key job responsibilities
You'll collaborate with world-class scientists and business partners to:
-Pioneer new approaches to experimentation/RCT science, automation and scalability
-Establish and elevate experimentation standards across Amazon's marketing ecosystem
-Drive alignment on experimentation best practices across partner teams and the Amazon science community
-Lead the development of uncertainty quantification and risk-based decision-making frameworks
-Support scientists in designing and analyzing high-impact experiments
A day in the life
A sample day....9:00 AM: Review and provide scientific guidance on a complex experiment design for a $100M brand marketing campaign. Partner with measurement scientists to refine their uncertainty quantification approach and ensure alignment best in class scientific RCT standards.
11:00 AM: Lead a working session with cross-functional teams to evaluate a new experimentation framework that could improve Signal Utilization metrics across multiple marketing measurement models. Collaborate with data scientists to develop more robust calibration methods.
1:00 PM: Host office hours for embedded scientists across marketing teams, providing guidance on experimental design challenges and helping interpret experiment metrics for their specific business contexts.
2:30 PM: Partner with business stakeholders to translate experimental results into actionable insights, helping them understand the Expected Decision Value and potential regret of various marketing investment scenarios.
4:00 PM: Contribute to the development of new Bayesian methods for marketing measurement, focused on improving how we quantify and communicate uncertainty in marketing decision-making.
5:00 PM: Collaborate with executive science leaders to evolve experimentation standards and best practices, ensuring we're continuously raising the bar on marketing measurement across Amazon.
About the team
Our team helps powers the decision-making engine behind Amazon's global marketing investments, building state of the art measurement systems that determine where and how Amazon spends every marketing dollar. We combine advanced causal inference, experimentation, and machine learning to create a closed-loop marketing decision system that optimizes spend across brand and performance marketing. Our innovations in measurement science directly impact Amazon's growth, helping surface the most relevant ads to customers while maximizing marketing ROI. We're pioneering new approaches to marketing measurement that are setting industry standards and transforming how one of the world's largest advertisers makes marketing decisions.
