Senior Applied Scientist, EU ATS Science and Tech - Optimization

Have you ever wondered how Amazon delivers timely and reliably hundreds of millions of packages to customer’s doorsteps? Are you passionate about data and mathematics, and hope to impact the experience of millions of customers? Are you obsessed with designing simple algorithmic solutions to very challenging problems?

If so, we look forward to hearing from you!

Amazon STEP Science and Tech is seeking Applied (or Research) Scientists. As a key member of the central Research Science Team of logistic operations, these persons will be responsible for designing algorithmic solutions based on data and mathematics for optimizing the end-to-end Amazon supply chain network.

The job is opened in the EU Headquarters in Luxembourg (alternatively: Barcelona or Berlin or London), designed to maximize interaction with the team and stakeholders.

Key job responsibilities
Solve complex optimization and machine learning problems using scalable algorithmic techniques.

Design and develop efficient research prototypes that address real-world problems in the massive logistics network of Amazon.

Lead complex time-bound, long-term as well as ad-hoc analyses to assist decision making.

Communicate to leadership results from business analysis, strategies and tactics.


A day in the life
You will be brainstorming algorithmic approaches with team-mates to solve challenging problems for Amazon logistics operations.

You will be developing and testing prototype solutions with above algorithmic techniques.

You will be scavenging information from the sea of Amazon data to improve these solutions.

You will be meeting with other scientists, engineers, stakeholders and customers to enhance the solutions and get them adopted.

About the team
The Science and Tech (SnT) team of EU STEP is looking for candidates who are looking to impact the world with their mathematical and data-driven skills.

We are the End-to-End Supply Chain optimizers. As the core research team, we grow Amazon's logistics business to support decision making in an increasingly complex ecosystem of a data-driven supply chain and e-commerce giant.

Our mathematical algorithms provide confidence in leadership to invest in programs of several hundreds millions euros every year.
Above all, we are having fun solving real-world problems, in real-world speed, while failing & learning along the way.
We use modular algorithmic designs in the domain of combinatorial optimization, solving complicated generalizations of core OR problems with the right level of decomposition, employing parallelization and approximation algorithms.

We use deep learning, bandits, and reinforcement learning to put data into the loop of decision making.

We like to learn new techniques to surprise business stakeholders by making possible what they cannot anticipate. For this reason, we work closely with Amazon scholars and experts from Academic institutions.

We code our prototypes to be production-ready

We prefer provably optimal solutions than heuristics, though we settle for heuristics when performance dictates it. Overall, we appreciate the value of correct modeling.

Basic Qualifications

- Master's degree
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
- Experience in building machine learning models for business application
- Experience in applied research

Preferred Qualifications

- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.

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