Senior Applied Scientist, GEOS Science Team

Amazon Inc.; Asti; Hybrid; EUR 141,000 - 201,000; Full time

Benefits offered by this job

Medical, Dental, and Vision Coverage

Maternity and Parental Leave Options

Paid Time Off (PTO)

401(k) Plan

Job summary

Amazon is seeking a Senior Applied Scientist for the GEOS Science Team within GOES to own optimization models behind REOS, modeling site locations and network costs over a 20-year horizon. You will implement Python solutions and collaborate with Finance, Product, and other teams to deliver actionable recommendations.

Basic qualifications include a Master's or PhD and 5+ years of applied research experience, with strong skills in C/C++, Python, Java or Perl.

Qualifications

• Master's degree or PhD is required.
• 5+ years of applied research experience.
• Knowledge of programming languages such as C/C++, Python, Java or Perl.

Responsibilities

• Formulate and solve large-scale optimization models — linear, mixed-integer, network flow and stochastic or robust variances.
• Select and engineer the solution methodology for production-scale models.
• Own modeling assumptions and the cost structure, defend to Finance.
• Implement and productionize models in Python against solvers and data pipelines.
• Engage customers directly, translate problems into formulations and deliver results.
• Lead end-to-end initiatives and mentor scientists on AI tooling and design reviews.

Skills

Applied research experience

Programming languages

Education

Master's or PhD

Job description

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-integer, network flow and stochastic or robust variants — that recommend site locations and network topology at lowest total cost.
• Select and engineer the solution methodology — decomposition, column generation, cutting planes, relaxations and heuristics — so models solve reliably at production scale and within business timelines.
• Own the modeling assumptions and the cost structure behind them, and make the trade-offs explicit and defensible to Finance.
• Implement and productionize models in Python against commercial and open-source solvers, with the data pipelines and validation that keep them trustworthy, and maintain them as the network and the data change.
• Engage customers directly: elicit the real problem, translate it into a formulation, run scenarios with them, deliver results, and explain what the model recommends and why.
• Lead initiatives end to end, apply AI tooling to the science workflow, and raise the bar for the scientists around you through design review and mentoring.

A day in the life

You might start your morning reviewing experiment results and refining a machine learning model before syncing with engineers on a production integration plan. After lunch, you could be white‑boarding a new algorithmic approach with colleagues from product and science teams, then drafting a research document that outlines your methodology and findings. You will regularly present your work to leadership, participate in science reviews, and stay connected to the latest research in your field to inform your next initiative.

Amazon Benefits:

• Medical, Dental, and Vision Coverage
• Maternity and Parental Leave Options
• Paid Time Off (PTO)
• 401(k) Plan

If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skill sets. If you’re passionate about this role and want to make an impact on a global scale,

About the team

Our team is focused on applying advanced science to solve high-impact problems that improve Amazon's products and customer experience. We value collaboration, intellectual curiosity, and a commitment to scientific rigor. You will work alongside researchers, engineers, and product managers in an inclusive environment where new ideas are encouraged and your contributions directly influence the direction of our roadmap. If you are looking for a place where research translates into real outcomes, we would love to see you.

Basic Qualifications

• Master's degree, or PhD
• 5+ years of applied research experience
• Knowledge of programming languages such as C/C++, Python, Java or Perl

Preferred Qualifications

• Experience using managed ML/AI solutions
• Experience in one or more of the following areas: data center or network capacity planning, data center or network infrastructure program/project management, or related technical fields
• Ph.D. in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science, or experience working on multi-team, cross-disciplinary projects
• Master's degree in engineering or equivalent, or experience using AI-assisted development tools in academic or professional settings
• Experience in data analysis, with the ability to interpret and draw insights from data to inform decision‑making processes
• Knowledge of data engineering pipelines, cloud solutions, ETL management, databases, visualizations and analytical platforms

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

The base salary range for this position is listed below. Your Amazon package will include sign‑on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

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