Principal Planning Integration, Fleet Demand, EU Fleet Supply Chain

Amazon Amazon · Big Tech · LU, Luxembourg · Buying, Planning, & Instock Management

The role focuses on re-engineering an automated forecasting model (Fleet Requirement Planning - FRP) for Amazon's EU fleet. Key responsibilities include integrating new van requirements, moving to clustering, probability-based modeling, and agentic AI, scaling optimization models with a focus on operational costs, evolving the user interface to a dashboard, integrating new supply channels, and scaling the FRP to Middle Mile. The role also involves developing a 'digital twin' for 'what-if' analysis and supporting critical business decisions.

What you'd actually do

  1. own the re-engineering of the current automated forecasting model, Fleet Requirement Planning [FRP]
  2. integrate new Amazon organization van requirements (e.g. Ship With Amazon (SWA) /Delivery Service Partner (DSP) Lite)
  3. move to clustering, probability-based modelling and agentic AI
  4. scale optimization models with objective function being the operational costs
  5. evolve the user interface from Excel based to a more friendly dashboard

Skills

Required

  • Experience implementing pragmatic operations solutions to solve problems such as the scheduling, routing, assignment, facility location, or lot-sizing problem
  • Experience with algorithm and model development work for large-scale applications
  • Experience with technology transformation initiatives
  • Experience leveraging technology to drive process improvements
  • Experience in strategic planning
  • Experience solving complex problems quantitatively and develop actionable data-driven business recommendations
  • Experience in technical support, or experience that includes strong analytical skills, attention to detail, and effective communication abilities
  • Experience with creating & improving a variety of processes across product types & teams
  • Experience coordinating complex products with stringent technical requirements, development cycles and schedules
  • Experience working across teams and influencing teams that are not your own
  • Experience communicating results to senior leadership
  • Experience dealing well with ambiguity, prioritizing needs, and delivering measurable results in an agile environment
  • Experience prioritizing competing demands, scoping large efforts, and negotiating timelines
  • Knowledge of supply chain management concepts - forecasting, planning, sourcing, optimization and logistics or equivalent
  • Experience leading cross-functional teams across engineering, operations, and field execution through launch readiness and go-live phases, or experience with retrofits, launches, or automation deployments
  • Experience delivering results, setting strategy, and running a large volume and high profile business

Nice to have

  • Master's degree, or a Bachelor's degree and experience with various machine learning techniques and parameters that affect their performance
  • Lean Six Sigma Green Belt or Black Belt certification

What the JD emphasized

  • agentic AI

Other signals

  • move to clustering, probability-based modelling and agentic AI
  • scale optimization models
  • develop a “digital twin” of the FRP models
  • perform “what if analysis”, multiple planning scenarios and investment analysis