Research Scientist, SCOT First Inbound
Posted
The Supply Chain Optimization Technologies (SCOT) organization owns Amazon’s global inventory management systems: we decide what, when, where, and how much we should buy to meet Amazon’s business goals and to make our customers happy. We do this for millions of items, for hundreds of product lines worth billions of dollars of inventory world-wide. Our systems are built entirely in-house, and are automated large-scale business operations, inventory and supply chain planning and optimization systems. We foster new game-changing ideas, creating ever more intelligent and self-learning systems to maximize the efficiency of Amazon's inventory investment and placement decisions.
The Inbound Systems team within SCOT seeks a Research Scientist to innovate in SCOT inbound placement decision and capacity management systems. As a research scientist, you will use your experience to initiate the design, development, execution and implementation of scientific research projects. Working closely with fellow scientists, BI engineers and product managers, you will use your experience in modeling, statistics, and simulation to design models of new policies, simulate their performance, and evaluate their benefits and impacts to cost, reliability, and speed of our fulfillment network. Our teams are looking for experience in network and combinatorial optimization, statistical inference, and machine learning. This position requires superior analytical thinking, and ability to apply their technical and statistical knowledge to identify opportunities for real world applications.
The Inbound Systems team within SCOT seeks a Research Scientist to innovate in SCOT inbound placement decision and capacity management systems. As a research scientist, you will use your experience to initiate the design, development, execution and implementation of scientific research projects. Working closely with fellow scientists, BI engineers and product managers, you will use your experience in modeling, statistics, and simulation to design models of new policies, simulate their performance, and evaluate their benefits and impacts to cost, reliability, and speed of our fulfillment network. Our teams are looking for experience in network and combinatorial optimization, statistical inference, and machine learning. This position requires superior analytical thinking, and ability to apply their technical and statistical knowledge to identify opportunities for real world applications.