Applied Science Manager, Jci Measurement and Optimization Science Team

Amazon Amazon · Big Tech · 13, Japan +1 · Applied Science

This role manages a team of applied scientists, economists, and data scientists focused on building causal models, optimization systems, and AI-driven analytics for supply chain cost reduction. The team's work directly influences VP-level investment decisions. Responsibilities include leading the team, setting the science roadmap, partnering with other departments, integrating science models into AI tools for non-technical stakeholders, and representing the science function to leadership.

What you'd actually do

  1. Lead and grow a team of scientists delivering causal models, optimization engines, and AI-driven insights for supply chain cost reduction
  2. Set the science roadmap and prioritize across workstreams: causal attribution, financial simulation, forecasting, and GenAI agent development
  3. Partner with product, engineering, operations, and finance to translate science into operational impact
  4. Drive the integration of science models into AI tools — making causal reasoning accessible to non-technical stakeholders at scale
  5. Represent CtS science to VP-level leadership through MBR/QBR mechanisms and OP planning

Skills

Required

  • Knowledge of ML, NLP, Information Retrieval and Analytics
  • Master's degree in econometrics, statistics, industrial engineering, operations research, optimization, data mining, analytics, or equivalent quantitative field
  • 3+ years of building machine learning models or developing algorithms for business application experience

Nice to have

  • Experience building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers
  • Experience communicating with users, other technical teams, and management to collect requirements, describe software product features, and technical designs

What the JD emphasized

  • GenAI agent development

Other signals

  • leading a team of scientists
  • setting science roadmap
  • partnering with product, engineering, operations, and finance
  • driving integration of science models into AI tools
  • representing CtS science to VP-level leadership