Senior Applied Scientist, GEOS Science Team
Pubblicato il 19-10-2026 - Amazon in Italia
Senior Applied Scientist, GEOS Science Team
Job ID: 10580142 | Amazon.com Services LLC
This role sits in Business Innovation and AI (BIA), part of Global Ops Enterprise Services (GOES), and works on the Real Estate Optimization System (REOS). REOS decides where Amazon builds. It models the supply chain from fulfillment centers through sort centers and delivery stations to the customer as a time-space network over a 20-year horizon, and ranks candidate sites on total network cost — transportation, labor, real estate and unmet demand — rather than on rent alone. Alongside the core engine we are building a family of AI tools that source candidate properties from across the market, assemble and validate the data behind a study, and generate the cost briefs that explain a recommendation, and we are extending the same optimization to zip-code-level Sub Same Day siting. You will work alongside scientists, and with partner teams in GOES Portfolio Management, Capacity Planning and Finance.
We are looking for an Applied Scientist to own the optimization models behind REOS.
You will formulate the models that decide where Amazon builds, engineer the solution methods that make them tractable at network scale, lead the initiatives that extend them, and take the recommendations to the Development Managers and Finance partners who act on them. The work is hands‑on and close to the decision: your models become the basis for real capital commitments.
The people who make those commitments are your customers. You will work with Development Managers, Capacity Planning teams and Finance partners to understand what they need, build and run the model that answers it, deliver the result, and explain what it recommends in terms they can act on and defend. Reporting to the Science Manager, you will own the technical approach on the models you build and mentor the scientists working alongside you.
Key job responsibilities
- Formulate and solve large-scale optimization models — linear, mixed-in