Applied Scientist, Eu Ats Science and Tech
il y a 7 jours
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).
- Predictive analytics (e.g., forecasting, time-series, neural networks)
- Prescriptive analytics (e.g., stochastic optimization, bandits, reinforcement learning).
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 Transportation Services is seeking Applied (or Research) Scientists. As a key member of the central Research Science Team of ATS operations, these persons will be responsible for designing algorithmic solutions based on data and mathematics for optimizing the middle-mile Amazon transportation 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.
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 middle-mile operations 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 the middle-mile operations of Amazon.
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
ATS stands for Amazon Transportation Service, we are the middle-mile planners: we carry the packages from the warehouses to the cities in a limited amount of time to enable the “Amazon experience”. As the core research team, we grow with ATS business to support decision making in an increasingly complex ecosystem of a data-driven supply chain and e-commerce giant.
We schedule more than 1 million trucks with Amazon shipments annually; our algorithms are key to reducing CO2 emissions, protecting sites from being overwhelmed during peak days, and ensuring a smile on Amazon’s customer lips.
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.
- Experience from working in a fast-paced applied research environment.
- Ability to handle ambiguity.
- Top tier publications pertinent to the field of study.
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