Senior Applied Scientist, CBA
Core
Developing transformer-based foundation models of customer behavior to estimate causal effects of customer actions on long-term value for Amazon's Retail business.
Role type
Senior Applied Scientist (Causal Inference & Sequence Modeling)
Builds
Shared infrastructure models for measurement and optimization systems across Amazon
Domain
E-commerce / Customer Analytics / Causal Inference
Deliverable
production ML models
Required skills
causal inference, sequence modeling, neural deep learning, Java, C++, Python, experimental design
Preferred skills
peer-reviewed scientific contributions in premier journals and conferences
Technologies
transformer-based models, deep learning frameworks
Responsibilities
Inventing methods to make learned representations estimation-aware, closing the loop between experimental ground truth and model training, extrapolating short-horizon observations into year-ahead causal effects, translating research into production systems
Seniority
Senior, hands-on IC