Applied Scientist II, EU STEP Science and Tech
Job ID : 2945206 | Amazon EU SARL (UK Branch)
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, Berlin or London), designed to maximize interaction with the team and stakeholders, but we will consider applicants with remote work requirements as well.
BASIC QUALIFICATIONS
PREFERRED QUALIFICATIONS
KEY JOB RESPONSIBILITIES
A DAY IN THE LIFE
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.
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