Are you passionate about applying causal inference and machine learning techniques to revolutionize how Amazon makes inventory decisions? Do you want to be part of a team that's reinventing how we measure the long-term impact of product availability on customer behavior throughout their entire shopping journey?
The Consumer Instock Value (CIV) team within Amazon's Supply Chain Optimization Technology (SCOT) Group develops and manages systems that estimate the long-term impact of inventory availability and delivery speed changes at the product level. Our estimates are crucial inputs for multiple production systems across Amazon's supply chain planning, helping teams make critical decisions about inventory management, selection, and placement.
We are seeking applied scientists to help shape our next-generation vision of modernizing how we estimate the impact of inventory decisions through innovative applications of machine learning and causal inference. We aim to create more accurate, scalable, and robust solutions that can adapt to Amazon's evolving customer shopping patterns and business needs.
Key responsibilities include:
- Developing innovative approaches that combine state-of-the-art AI/ML with causal inference to estimate individual treatment effects
- Creating sophisticated frameworks that capture customer journey interactions and cross-product patterns using sequential data
- Designing and implementing validation approaches using experiments, quasi-experimental methods, and simulations
- Building scalable architectures capable of processing multiple customer interaction data streams
- Collaborating with other scientists and technical teams to implement production systems that can process complex customer journey data
- Leading research initiatives to resolve scientific ambiguities in applying ML methods to causal inference problems
- Presenting findings to stakeholders and contributing to Amazon's research paradigms
The ideal candidate will have:
- Deep expertise in causal inference and machine learning
- Experience with large-scale data processing and modeling
- Strong research background in econometrics or related fields
- Ability to bridge theoretical foundations with practical implementations
- Excellence in communicating complex technical concepts to diverse audiences
Your work will directly impact critical business decisions across Amazon's retail business, helping optimize the balance between inventory costs and customer experience while contributing to scientific developments in the intersection of machine learning and causal inference.
