Applied Scientist, SCOT FO - SnT

Amazon Inc.; Barcelona; On-site; EUR 90,000 - 140,000; Full time

Job summary

Amazon in Barcelona, with options for Luxembourg or London, seeks Applied Scientists to advance SCOT Fulfillment Optimization. You will design ML and optimization models powering fulfillment decisions at global scale, focusing on large-scale optimization, planning, and predictive ML.

Candidates should have a strong background in optimization, ML, and time-series forecasting, and be comfortable executing research prototypes that become production systems used for millions of packages daily.

Qualifications

• PhD or Master's degree in CS, CE, ML or related field.
• Publications or patents at top venues preferred.
• Experience building models for business applications.
• Experience in algorithms, data structures, numerical optimization.

Responsibilities

• Design and implement optimization and forecasting models for large-scale fulfillment problems.
• Build research prototypes end-to-end from problem formulation to production validation.
• Analyze tradeoffs (cost, speed, capacity, accuracy) and present actionable recommendations.
• Collaborate with engineers to ship science solutions to production systems.

Skills

Optimization theory

Machine learning

Time-series forecasting

Operations research

Probabilistic forecasting

Education

PhD in CS/CE/ML

MS in CS/CE/ML

Tools

Java

C++

Python

Gurobi/CPLEX

Job description

Description

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.

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

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

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.

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.

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