Applied Scientist, Pricing Science
Core
Build production-scale causal ML pipelines to power Amazon's pricing decisions, bridging econometric analysis and machine learning to optimize customer lifetime value and pricing accuracy.
Role type
Senior Applied Scientist (Causal Inference & Pricing)
Builds
Causal estimation models, analysis workflows for pricing weblabs, and reusable causal ML infrastructure
Domain
E-commerce, Retail, Machine Learning, Causal Inference
Deliverable
production ML models
Required skills
Causal inference, CATE estimation, econometric analysis, experimental design, model evaluation, Python, Java/C++, algorithms and data structures, numerical optimization, distributed computing
Preferred skills
Unix/Linux, professional software development, generative AI tools
Technologies
Python, Java, C++, Unix/Linux
Responsibilities
Design, train, evaluate, and deploy end-to-end causal estimation models for pricing; serve as SME on causal ML methodology and identification strategies; contribute causal analysis to pricing weblab and A/B test post-analysis; define business metrics for model evaluation; assess and adopt novel causal inference techniques; write internal documentation and methodology papers; collaborate with economists, software engineers, and product managers
Seniority
Senior, hands-on IC