Applied Science Manager, JCI Measurement and Optimization Science Team
Core
Lead the Cost-to-Serve science team to build causal models, optimization systems, and AI-driven analytics that identify, explain, and quantify supply chain cost-saving opportunities for Amazon Japan.
Role type
Applied Science Manager (Supply Chain Optimization & Causal Inference)
Builds
Causal models, optimization engines, and GenAI-powered analytics tools for supply chain cost reduction
Domain
Supply Chain Management, Operations Research, Causal Inference, Generative AI
Deliverable
production ML models
Required skills
Causal inference, optimization algorithms, machine learning, NLP, information retrieval, financial simulation, supply chain forecasting, GenAI agent development, team leadership, roadmap planning, cross-functional partnership
Preferred skills
Deep learning, computer vision, complex software system delivery, technical design communication
Technologies
GenAI, causal inference frameworks, optimization solvers, NLP libraries
Responsibilities
Lead and grow a team of scientists delivering causal models and optimization engines; Set the science roadmap across causal attribution, financial simulation, forecasting, and GenAI; Partner with product, engineering, operations, and finance to translate science into operational impact; Drive integration of science models into AI tools for non-technical stakeholders; Represent science to VP-level leadership through MBR/QBR mechanisms
