Amazon's Marketing Science team (a part of Customer Behavior Analytics) is looking for a Principle Research Scientist with strong technical skills in causal inference to drive methodology improvements for marketing measurement using customer-level data.
You will develop new models using customer-level panel data on advertising exposure to identify the causal impact of Amazon marketing. You will work closely with media and portfolio teams to understand business needs and with a sister engineering team to productionalize solutions. You will work with other science teams on developing cross channel measurement to understand fixed and variable channel interactions. You will help drive incrementality tests for model validation and improvement. Your models will improve the efficiency of billions of dollars of marketing spend.
This role requires an individual with strong quantitative modeling skills and the ability to apply statistical/machine learning, econometric, and experimental design methods.
The candidate should have strong communication skills to work closely with stakeholders to translate business needs into methodology and data-driven findings into actionable insights. The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail, and ability to work in a fast-paced and ever-changing environment.
Overall, the candidate’s responsibilities include:
· Design and build scalable analytic solutions using econometric models to measure the financial impact of cross channel marketing spend
· Work with the data acquisition team on data requirements
· Experience with marketing measurement and understanding of fixed marketing channels
· Experience working with engineering to productionalize econometric models
· Expertise in ML methods
Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.
Ideal candidate profile
· PhD in Economics or related discipline.
· Expertise in causal analysis
· 6+ years of professional experience in machine learning and or economics (industry, government, and academia).
· Proven experience in design and execution of analytical projects
· Coding skills for data analysis in Stata, R, Scala, or Python
· Strong communication skills