Applied Scientist II, SCOT-Optimal Sourcing Systems, Buying Science Team
Core
Design and deploy optimization, causal inference, and ML solutions to optimize inventory sourcing, vendor orchestration, and supply chain flows for Amazon's global business.
Role type
Senior Applied Scientist (Supply Chain Optimization & ML)
Builds
Next-generation sourcing and vendor experience systems for millions of products and billions of units.
Domain
Supply chain management, operations research, and machine learning.
Deliverable
production ML models
Required skills
Mathematical optimization, causal inference, sequential decision-making (RL/MDP), stochastic modeling, Python, production-quality scientific software development, supply chain management concepts (forecasting, planning, sourcing, logistics).
Preferred skills
Reinforcement learning frameworks, deep learning frameworks, optimization solvers, experimental design in operational settings.
Technologies
Gurobi, OR-Tools, RLlib, Stable Baselines, PyTorch, TensorFlow.
Responsibilities
Set scientific strategic vision and roadmap; frame research challenges and invent novel methodologies; write critical-path code for scientifically-complex software solutions; deploy novel models into production; develop reusable science components; influence business and engineering strategies.
Seniority
Senior, hands-on IC with strategic leadership
