AmazonPosted 2d ago

Applied Scientist, SCOT FO - SnT

Applied Scientist, SCOT FO - SnT at Amazon scores 90 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.

ES, B, Barcelonaseniorfull-time

AI in this role

Design and build large-scale machine learning and optimization models for Amazon's fulfillment network and demand forecasting.

python

machine-learningmathematical-programmingcombinatorial-optimizationtime-series-forecastingcausal-inference

How does Amazon decide which fulfillment center ships your order, which truck carries it, and how to keep promises across hundreds of millions of packages daily? How does it decide how many trucks and how much labor are required to ship orders across the network?

SCOT Fulfillment Optimization (FO) owns the optimization and forecasting science behind these decisions.

We are seeking Applied Scientists to join the FO Science & Tech team in Barcelona (alternatively: Luxembourg or London) with a strong academic background in optimization, machine learning, and/or time-series forecasting.

• You will design and build state-of-the-art machine learning and optimization models that power Amazon's fulfillment decisions at an unprecedented scale across two core scientific pillars:

• Large-Scale Optimization and Planning: Designing planning systems for order assignment and resource utilization, while balancing multi-objective cost-speed tradeoffs to enable controllers to steer millions of shipments per hour optimally.

• Demand Forecasting & Predictive ML: Developing time-series forecasts for customer demand, incorporating contextual information (weather, sales, order properties), and modeling uncertainty for core planning systems.

Basic qualifications

• PhD in Operations Research, Applied Mathematics, Computer Science, or related field (or equivalent experience)

• Strong programming skills (Python preferred; experience with optimization solvers a plus)

• Research experience in one or more:

• Large-scale mathematical programming (LP, MIP, decomposition methods)

• Combinatorial optimization (assignment, scheduling, network flows)

• Multi-objective optimization and control

• Large-scale time-series forecasting (GenAI models, probabilistic forecasting, uncertainty quantification)

• Causal inference (spatiotemporal causal modeling, offline policy evaluation)

Preferred qualifications

• Experience building optimization systems that run in production at scale
• Being comfortable with ambiguity and fast iteration cycles
• Publications in relevant venues

Key job responsibilities
Design and implement optimization and forecasting models for large-scale fulfillment problems, from order assignment to network flow control. Build research prototypes end-to-end: from problem formulation through scalable implementation to production validation. Analyse complex tradeoffs (cost, speed, capacity, accuracy) and translate findings into actionable recommendations for leadership and operations teams. Collaborate with engineers to bring science solutions into production systems serving millions of customer orders daily.

A day in the life
You formulate an optimization or forecasting problem on a whiteboard with teammates, then prototype it in Python with real data by the afternoon. You run experiments against production-scale datasets, iterate on the model, and present results to stakeholders who will use them to make network decisions next week. Some days you dive deep into solver performance; other days you're explaining a Pareto frontier to an operations leader. You collaborate with large engineering and product teams to bring your solutions into systems serving millions of customers. Alongside fast-turnaround prototypes, you own long-term research bets, the kind that reshape how Amazon's fulfillment network operates at scale. Your work goes live.

About the team
SCOT Fulfillment Optimization Science & Tech (FO SnT) is the applied research team behind Amazon's fulfillment decision-making systems. We decide how orders get assigned to warehouses, how capacity is allocated across the network, and how cost and speed tradeoffs are managed in real time, at global scale. Our models influence billions of euros in annual operational spend. They protect sites from overload during peak, reduce transportation costs and CO2 emissions, and ensure customers receive their packages when promised. Leadership relies on our science to make investment decisions worth hundreds of millions. We are practitioners of large-scale optimization: MIP formulations, decomposition methods, approximation algorithms, and parallelisation. We use machine learning where it sharpens our decisions, including forecasting, learned heuristics, and multi-armed bandits. We pick the right tool for the problem, not the fashionable one. You will work alongside Senior and Principal scientists, and collaborate with Amazon Scholars and academic partners who bring frontier research into our applied problems. We code our prototypes to be production-ready and collaborate with large engineering teams to ship systems, not papers. Above all, we have fun solving hard real-world problems at real-world speed, failing, learning, and shipping along the way.

Basic qualifications

- PhD, or a Master's degree and experience in CS, CE, ML or related field
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience programming in Java, C++, Python or related language
- Experience in building models for business application
- 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 opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.

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.

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Skills and AI tools this role asks for

Machine LearningMathematical ProgrammingCombinatorial OptimizationTime Series ForecastingCausal InferencePython

Questions you could be asked

Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where mathematical programming was part of your work. What did you do?
Tell me about a project where combinatorial optimization was part of your work. What did you do?
Tell me about a project where time series forecasting was part of your work. What did you do?
Tell me about a project where causal inference was part of your work. What did you do?

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List these exact terms on your resume: Machine Learning, Mathematical Programming, Combinatorial Optimization, Time Series Forecasting, and Causal Inference. An applicant tracking system matches the wording, not the idea.
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Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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