Job description

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 whiteboarding 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: Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 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, please apply! 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 hear from you. - Master's degree, or PhD - 5+ years of applied research experience - Knowledge of programming languages such as C/C++, Python, Java or Perl - 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 amazon.jobs 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 amazon.jobs . USA, FL, Miami - 167,100.00 - 226,100.00 USD annually USA, TN, Nashville - 158,800.00 - 214,800.00 USD annually USA, WA, Bellevue - 167,100.00 - 226,100.00 USD annually

Requirements

Must-haves

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
About the team
Master's degree, or PhD
5+ years of applied research experience
Knowledge of programming languages such as C/C++, Python, Java or Perl
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.
USA, FL, Miami - 167,100.00 - 226,100.00 USD annually
USA, TN, Nashville - 158,800.00 - 214,800.00 USD annually
USA, WA, Bellevue - 167,100.00 - 226,100.00 USD annually

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Keywords to mirror

aipythonmachine learningjavaml

Platform: amazon
Effort: medium

Benefits

Benefits detected in the listing.

Health Insurance

Dental

Vision

401(K)

Pto

Paid Time Off

Parental Leave