Have you ever wondered how Amazon delivers timely and reliably hundreds of millions of packages to customer’s doorsteps?
Are you passionate with 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 an Operations Research & Machine Learning Scientist to be based in the EU Headquarters in Luxembourg.
As a key member of the Research Science Team, this person will be responsible for designing algorithmic solutions based on data and mathematics for optimizing the middle-mile Amazon Transportation Network.
This person is expected to convince cross-functional leadership and manage multiple stakeholders. The successful applicant will ensure that our end-to-end strategies in terms of customer demand fulfillment, routing, consolidation locations, linehaul / airhaul options and last-mile transportation are streamlined and optimized.
This critical role requires an aptitude for independent initiative and decision-making, the ability to drive innovation in modelling and optimization across Amazon’s expanding European network and linking into global initiatives and expansion strategies.
Tasks / Responsibilities
Partner with the planning, linehaul / airhaul and sort center operations teams, while working closely with last-mile, supply chain, and global delivery departments for modeling and optimizing the transportation network of EU using data.
Design, implement and support algorithmic prototypes and machine learning models for standardized processes.
Contribute to mid and long-term strategic transportation planning process.
Lead complex time-bound, long-term as well as ad-hoc transportation modelling analyses to help management in the decision making process.
Communicate with leadership regarding the results of business analysis, strategies and tactics.
Drive large-scale projects that will help scale and improve Amazon’s EU transportation network.
PhD in Operations Research, Machine Learning, Statistics, Applied Mathematics, Engineering, Computer Science or related field with at least eight years of relevant experience.
Project management experience working with tech and other cross-functional teams.
The ability to communicating technically, at a level appropriate to the audience.
Experience designing and implementing optimization models for discrete optimization problems (e.g., scheduling, vehicle routing, and facility location) and experience with leveraging such models to provide guidance for strategic and tactical business decision making.
Experience designing and implementing machine learning models for predictive and prescriptive analytics (e.g., forecasting time-series, neural networks, bandits, reinforcement learning) and experience with leveraging such models to provide guidance for strategic and tactical business decision making.
Comfortable to tradeoff complexity and efficiency of solution methodologies, according to the requirements of the problem.
Excellent written and verbal communication skills with both technical and business people.
Strong problem solving skills and ability to deal with ambiguity.
Detailed knowledge of optimization methods including linear and mixed-integer programming, network modeling, constraint programming, approximation algorithms, and advanced heuristic techniques.
Deep understanding of MIP strategies to customize and leverage commercial algorithms and adapt them as required.
Detailed knowledge of forecasting techniques with time-series tools, including ARIMA models, exponential smoothing, LSTM, CNNs.
Deep understanding of policy optimization techniques, including reinforcement learning, deep Q-learning, bandits, and online optimization.
Experience prototyping and developing software in traditional programming languages (C++ / Java / C#)
Experience implementing models and analysis tools through the use of high-level modeling languages (e.g. R, Julia, Matlab as examples).
Experience collecting, processing and combining big data with appropriate methodologies (e.g. Hadoop, Map-Reduce)