Applied Scientist , Inbound Systems

Amazon; Atlanta (GA); On-site; USD 172,400 - 223,400; Full time

Job summary

Amazon.com Services LLC is seeking a senior scientist to design and deploy large-scale optimization and forecasting models that shape inventory placement and flow across our fulfillment network. You will work on decomposition of network problems, scalability, and production-grade software, partnering with multidisciplinary teams to deliver measurable impact.

Ideal candidates have 3+ years building business models, a PhD or MS with 4+ years in CS/CE/ML, and strong coding skills in Java, C++, and

Qualifications

• 3+ years building models for business applications.
• PhD, or MS plus 4+ years in CS/CE/ML or related field.
• Patents or publications in top-tier venues are a plus.
• Experience programming in Java, C++, Python.

Responsibilities

• Design and deploy large-scale optimization and forecasting models for inventory planning.
• Shape decomposition and coordination of network problems for scalable planning.
• Improve solver performance and production-grade deployment.
• Collaborate with science, engineering, operations and finance partners.
• Deliver end-to-end solutions and communicate results to leadership.
• Publish and apply cutting-edge OR/ML techniques within the team.
• Lead analyses and drive practical impact across Amazon's supply chain.

Skills

Java

C++

Python

Algorithms

Distributed computing

Education

PhD or MS with 4+ years in CS/CE/ML

Tools

Unix/Linux

Job description

Job ID: 10483032 | Amazon.com Services LLC - A57

Amazon's Supply Chain is the backbone of the fastest growing e-commerce business in the world, and planning it is one of the largest optimization problems in industry. Every week we decide where millions of products should sit across hundreds of fulfillment centers, how inventory should flow between suppliers, buildings, and customers, and how to balance cost, speed, and capacity, all under deeply uncertain demand.

No single model can solve a problem this large. The Supply Chain Planning Optimization team is building the next generation of planning systems around large-scale distributed optimization: decomposing the full network problem into tractable pieces that coordinate toward a globally consistent plan, solving optimization problems with hundreds of thousands of variables in seconds, and pairing them with probabilistic forecasts so plans hold up when reality diverges from the forecast. The work spans the full stack of modern operations research, from decomposition and convergence to stochastic optimization and solver performance at scale. And it has a rare property: the models you build move real inventory for hundreds of millions of customers, and you see the results in the physical world within weeks.

What you'll do

• Design and deploy large-scale optimization and forecasting models that plan inventory placement and flow across our EU/NA fulfillment network under uncertainty
• Shape how the full network problem is decomposed and coordinated, defining the mathematical architecture of the planning system rather than just the models within it
• Push the computational frontier through formulations that solve fast and reliably at scale, and through the solver technology and tooling that make experimentation cheap
• Work with science, engineering, operations, and finance partners to take ideas from whiteboard to production, then own them end to end once live

What we're looking for

An experienced scientist with depth in large-scale optimization (stochastic optimization and decomposition methods especially welcome), fluency in machine learning and probabilistic forecasting, and a track record of delivering complex scientific systems end to end. You care about both the elegance of a formulation and whether it solves in two seconds or two hundred, and you're energized by delivering incremental wins while building toward a long-term scientific vision.

If you want your optimization theory to move real inventory at planetary scale, this is the team.

Key job responsibilities

• Build state-of-the-art, robust, and scalable stochastic optimization and probabilistic forecasting algorithms that drive optimal planning and execution under uncertainty across Amazon's end-to-end supply chain
• Shape how large-scale planning problems are formulated, decomposed, and solved - designing for computational performance and reliability at the scale of Amazon's fulfillment network
• Engineer your algorithms as production-grade, cloud-native software, applying modern development practices from prototype through deployment
• Think several steps ahead: architect long-term scientific solutions while continuously shipping incremental improvements to what's already running
• Prototype fast, drive early adoption through pilots, integrate operational feedback, and iterate
• Deliver your science into production by partnering closely with internal customers — understanding their needs and blockers, and influencing their roadmaps
• Lead complex analyses and communicate results and recommendations crisply to senior leadership
• Stay at the frontier as an active member of the science community: research, apply, and publish (internally and externally) the latest OR/ML techniques from academia and industry

About the team

We are a team of scientists and engineers who believe that some of the hardest optimization problems in the world are hiding inside everyday questions like "where should this product sit so a customer gets it tomorrow?" We take ideas from the frontier of operations research and machine learning - distributed optimization, planning under uncertainty, solving at massive scale - and turn them into systems that steer one of the largest supply chains on Earth. If a model we ship on Monday moves millions of units by Friday, that's a normal week; that loop between theory and the physical world is why we're here.

Basic Qualifications

• 3+ years of building models for business application experience
• PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
• Experience in patents or publications at top-tier peer-reviewed conferences or journals
• Experience programming in Java, C++, Python or related language
• Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Preferred Qualifications

• Experience using Unix/Linux
• Experience in professional software development

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.

USA, NY, New York - 172,400.00 - 223,400.00 USD annually

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