Applied Scientist, Eu Step Science And Tech

- PhD, or a Master's degree and experience applying theoretical models in an applied environment - Experience in patents or publications at top-tier peer-reviewed conferences or journals - Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing - Experience programming in Java, C++, Python or related language
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. Basic qualifications * PhD in Operations Research, Machine Learning, Statistics, Applied Mathematics, Computer Science or other field related to algorithms and data (or equivalent experience). * Excellent written and verbal communication skills. * Experience with some programming language (Java/Python/C++) * Research experience in one or more: *Combinatorial optimization problems (e.g., scheduling, vehicle routing, facility location). *Continuous optimization problems (e.g., linear programming, convex programming, non-convex programming). Preferred qualifications * Experience from working in a fast-paced applied research environment. * Ability to handle ambiguity. * Top tier publications pertinent to the field of study. 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.