The Pricing and Promotions Optimization Science team is hiring an incrementality applied scientist with experience in causal inference, experimentation, and ML development to help us expand our causal modeling solutions for understanding promotion effectiveness. Our work is foundational to providing seller-facing promotional tools, furthering internal research & development, and building out Amazon's promotion optimization measurement offerings. Incrementality measurement is a lynchpin for the next generation of Amazon Promotion solutions and this role will play a key role in the release and expansion of these offerings.
- Partner with principals and senior team members to drive science improvements and implement technical solutions at the state-of-the-art of machine learning and econometrics
- Partner with engineering and other science collaborators to design, implement, prototype, deploy, and maintain large-scale causal ML models.
- Carry out in-depth research and analysis exploring promotion-related data sets, including large sets of real-world experimental data, to understand behavior, highlight model improvement opportunities, and understand shortcomings and limitations.
- Define data quality standards for understanding typical behavior, capturing outliers, and detecting model performance issues.
- Work with product stakeholders to help improve our ability to provide quality measurement of promotion effectiveness for our customers.
About the team
The Pricing and Promotions Optimization science team owns price quality, discovery and discount optimization initiatives across Amazon’s internal pricing and promotions architectures as well as upwards into the customer discovery funnel. We leverage planet scale data on billions of Amazon and external competitor products to build advanced optimization models for pricing, elasticity estimation, product substitutability, and optimization. We preserve long term customer trust by ensuring Amazon's prices and promotions are always competitive and error free.
