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Amazon’s Middle Mile Science group is looking for a Senior Applied Scientist to build machine learning and optimization models to support pricing and revenue management of its external freight business. This includes the development of novel forecasting and dynamic pricing models, as well as the application of causal inference and artificial intelligence techniques, to improve marketplace services and execution for our customers. The Middle Mile Science group develops optimization and machine learning systems that power Amazon's freight transportation network, from network design and pricing to real-time load planning and capacity utilization. The scale of Amazon's fulfillment operations requires robust transportation networks that minimize cost while meeting all customer deadlines. Real-time execution depends on state-of-the-art optimization and artificial intelligence to coordinate thousands of operators and drivers. This includes shipper-facing and carrier-facing marketplace algorithms as well as network planning and optimization tools. Amazon often finds that existing techniques do not match our unique business needs,driving the innovation of new approaches and algorithms. As a Sr. Applied Scientist responsible for middle mile transportation, you will be working closely with different teams including business leaders and engineers to design and build scalable products operating across multiple transportation modes. You will create experiments and prototype implementations of new learning algorithms and prediction techniques. You will have exposure to top level leadership to present findings of your research. You will also work closely with other scientists and engineers to implement your models within our production system. You will implement solutions that are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility, and make decisions that affect the way we build and integrate algorithms across our product portfolio.
Job Responsibility:
Building machine learning and optimization models to support pricing and revenue management of external freight business
Development of novel forecasting and dynamic pricing models
Application of causal inference and artificial intelligence techniques to improve marketplace services and execution
Designing and building scalable products operating across multiple transportation modes
Creating experiments and prototype implementations of new learning algorithms and prediction techniques
Implementing solutions exemplary in algorithm design, clarity, model structure, efficiency, and extensibility
Requirements:
5+ years of building machine learning models or developing algorithms for business application experience
PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field, or Master's degree and 10+ years of industry or academic research experience
Experience programming in Java, C++, Python or related language
Experience in computer science fundamentals (object-oriented design, data structures, algorithm design, problem solving and complexity analysis)
Nice to have:
Experience with popular deep learning frameworks such as MxNet and Tensor Flow
Significant peer-reviewed scientific contributions in premier journals and conferences
Hands-on experience with reinforcement learning and/or dynamic programming
Experience working with AWS technologies
Experience applying causal inference and/or experimental design to drive business decisions in large-scale systems