Applied Scientist Ii, Scot-optimal Sourcing Systems, Buying Science Team

Amazon Amazon · Big Tech · Bellevue, WA · Applied Science

Applied Scientist role focused on building and deploying machine learning and optimization models for Amazon's supply chain systems, specifically for sourcing and vendor experience. The role involves setting scientific strategy, developing novel methodologies, and writing production-quality code for scalable business applications.

What you'd actually do

  1. Set the scientific strategic vision for the team. Lead problem decomposition and roadmap development.
  2. Identify and frame research challenges in ambiguous problem areas; invent novel methodologies to address them. Distinguish between problems requiring novel solutions versus those addressable with existing approaches.
  3. Exercise sound judgment to prioritize between short-term vs. long-term and business vs. technology needs.
  4. Set an example with exemplary scientific analyses; maintainable, well-tested code; and simple, effective solutions.
  5. Drive the design of scientifically-complex software solutions, personally writing critical-path code that embodies scientific novelty. Deploy novel models into production with a track record of impactful delivery.

Skills

Required

  • PhD in operations research, applied mathematics, theoretical computer science, or equivalent, or Master's degree and 4+ years of machine learning, statistical modeling, data mining, and analytics techniques experience
  • Knowledge of supply chain management concepts - forecasting, planning, sourcing, optimization and logistics or equivalent
  • Experience building and deploying machine learning or optimization models for business applications at scale
  • Proficiency in Python with experience developing production-quality scientific software
  • Technical depth in one or more of: mathematical optimization, causal inference, sequential decision-making (RL/MDP), or stochastic modeling

Nice to have

  • Publication record in operations research, machine learning, or a related quantitative field
  • Experience with optimization solvers (e.g., Gurobi, OR-Tools) or reinforcement learning frameworks (e.g., RLlib, Stable Baselines)
  • Experience with deep learning frameworks (e.g., PyTorch, TensorFlow)
  • Experience designing and analyzing experiments in operational or supply chain settings

What the JD emphasized

  • Deploy novel models into production with a track record of impactful delivery.
  • Experience building and deploying machine learning or optimization models for business applications at scale
  • Technical depth in one or more of: mathematical optimization, causal inference, sequential decision-making (RL/MDP), or stochastic modeling

Other signals

  • Deploy novel models into production with a track record of impactful delivery.
  • Experience building and deploying machine learning or optimization models for business applications at scale
  • Technical depth in one or more of: mathematical optimization, causal inference, sequential decision-making (RL/MDP), or stochastic modeling