Applied Scientist, EU STEP Science and Tech

Applied Scientist, EU STEP Science and Tech

Have you ever wondered how Amazon delivers hundreds of millions of packages reliably and on time? Are you passionate about data, mathematics, and designing simple yet effective algorithmic solutions to complex problems? If so, we want to hear from you!

Amazon STEP Science and Tech seeks Applied (or Research) Scientists to join our central Research Science Team in logistics operations. You'll design data-driven and mathematical algorithmic solutions to optimize Amazon's end-to-end supply chain network.

This position is based at our EU Headquarters in Luxembourg, with options in Barcelona, Berlin, or London, designed to maximize team interaction. Remote work options are also considered.

Basic Qualifications

• PhD in Operations Research, Machine Learning, Statistics, Applied Mathematics, Computer Science, or related fields, or equivalent experience.
• Excellent written and verbal communication skills.
• Experience programming in Java, Python, C++, or similar languages.
• Research experience in areas such as combinatorial optimization, continuous optimization, or related fields.

Preferred Qualifications

• Experience in a fast-paced applied research environment.
• Ability to handle ambiguity.
• Top-tier publications in relevant fields.

Key Responsibilities

• Solve complex optimization and machine learning problems using scalable algorithms.
• Design and develop prototypes addressing real-world logistics challenges.
• Lead analyses to support decision-making and communicate results to leadership.

A Day in the Life

You will brainstorm algorithmic approaches, develop and test prototypes, analyze Amazon data, and collaborate with scientists, engineers, and stakeholders to implement solutions.

About the Team

The EU STEP Science and Tech team focuses on optimizing Amazon's logistics through advanced mathematical algorithms and data-driven techniques. We work closely with academic experts, employ cutting-edge methods like deep learning and reinforcement learning, and prioritize correct modeling and production-ready prototypes. Our goal is to support strategic investments and solve real-world problems efficiently and innovatively.