Amazon
Applied Scientist , Automated Planning System
Amazon, New York, New York, United States, 10001
Applied Scientist
Amazon Supply Chain forms the backbone of the fastest growing e-commerce business in the world. The sheer growth of the business and the company's mission "to be Earth's most customer-centric company" makes the customer fulfillment business bigger and more complex with each passing year. The Supply Chain Planning Optimization team is looking for an exceptionally talented Scientist to tackle complex and ambiguous optimization and forecasting problems for our EU/NA fulfillment network. The team owns the optimization of our Supply Chain from our suppliers to our customers. We are also responsible for analyzing the performance of our Supply Chain end-to-end and deploying Operations Research, Machine Learning, Statistics and Econometrics models to improve decision making within our organization, including forecasting, planning and executing our network. We work closely with Supply Chain Optimization Technology (SCOT) teams, who own the systems and the inputs we rely on to plan our networks, the worldwide scientific community, and with our internal stakeholders within Supply Chain, Transportation, Store and Finance. We are looking for an experienced candidate having a well-rounded technical/science background, with a particular expertise in stochastic optimization, machine learning and probabilistic forecasting, as well as a history of delivering complex scientific projects end-to-end, and is comfortable in developing long term scientific solutions while ensuring the continuous delivery of incremental model improvements and results in an ever-changing operational environment. As an Applied Scientist, you will design, develop and deploy robust and scalable scientific solutions via Operations Research and Machine Learning algorithms, especially in the context of stochastic customer demand and other sources of uncertainty requiring to move past deterministic optimization. You will partner with other tech and science teams, operations, finance to identify opportunities to improve our processes in order to drive efficiency improvements in our Fulfillment Center network flows. This role requires a self-starter aptitude for independent initiative and the ability to influence partner scientific and operational teams so to drive innovation in supply chain planning and execution. You are passionate, results-oriented, and inventive scientist who obsesses over the quality of your solutions and their fast and scalable implementation to address and anticipate customer needs. Key Job Responsibilities
Build state-of-the-art, robust and scalable Stochastic Optimization and Probabilistic Forecasting algorithms to drive optimal planning under uncertainty and execution in Amazon end-to-end supply chain Design and engineer algorithms using Cloud-based state-of-the-art software development techniques Think multiple steps ahead and develop for long term solutions while continuously delivering incremental improvements to existing ones Prototype fast, ensure early adoption via pilots, integrate feedback into the models, and iterate Operationalize (i.e. deliver) your science solutions by closely partnering with internal customers, understand their needs/blockers and influence their roadmap Lead complex analysis and clearly communicate results and recommendations to leadership Act as an active member of the science community by researching, applying and publishing internally/externally the latest OR/ML techniques from both academia and industry
Amazon Supply Chain forms the backbone of the fastest growing e-commerce business in the world. The sheer growth of the business and the company's mission "to be Earth's most customer-centric company" makes the customer fulfillment business bigger and more complex with each passing year. The Supply Chain Planning Optimization team is looking for an exceptionally talented Scientist to tackle complex and ambiguous optimization and forecasting problems for our EU/NA fulfillment network. The team owns the optimization of our Supply Chain from our suppliers to our customers. We are also responsible for analyzing the performance of our Supply Chain end-to-end and deploying Operations Research, Machine Learning, Statistics and Econometrics models to improve decision making within our organization, including forecasting, planning and executing our network. We work closely with Supply Chain Optimization Technology (SCOT) teams, who own the systems and the inputs we rely on to plan our networks, the worldwide scientific community, and with our internal stakeholders within Supply Chain, Transportation, Store and Finance. We are looking for an experienced candidate having a well-rounded technical/science background, with a particular expertise in stochastic optimization, machine learning and probabilistic forecasting, as well as a history of delivering complex scientific projects end-to-end, and is comfortable in developing long term scientific solutions while ensuring the continuous delivery of incremental model improvements and results in an ever-changing operational environment. As an Applied Scientist, you will design, develop and deploy robust and scalable scientific solutions via Operations Research and Machine Learning algorithms, especially in the context of stochastic customer demand and other sources of uncertainty requiring to move past deterministic optimization. You will partner with other tech and science teams, operations, finance to identify opportunities to improve our processes in order to drive efficiency improvements in our Fulfillment Center network flows. This role requires a self-starter aptitude for independent initiative and the ability to influence partner scientific and operational teams so to drive innovation in supply chain planning and execution. You are passionate, results-oriented, and inventive scientist who obsesses over the quality of your solutions and their fast and scalable implementation to address and anticipate customer needs. Key Job Responsibilities
Build state-of-the-art, robust and scalable Stochastic Optimization and Probabilistic Forecasting algorithms to drive optimal planning under uncertainty and execution in Amazon end-to-end supply chain Design and engineer algorithms using Cloud-based state-of-the-art software development techniques Think multiple steps ahead and develop for long term solutions while continuously delivering incremental improvements to existing ones Prototype fast, ensure early adoption via pilots, integrate feedback into the models, and iterate Operationalize (i.e. deliver) your science solutions by closely partnering with internal customers, understand their needs/blockers and influence their roadmap Lead complex analysis and clearly communicate results and recommendations to leadership Act as an active member of the science community by researching, applying and publishing internally/externally the latest OR/ML techniques from both academia and industry