Amazon.com strives to be Earth's most customer-centric company where customers can shop in our stores to find and discover anything they want to buy. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment. Applied scientist at Amazon partner closely with senior management, business stakeholders, scientist and engineers to solve key business problems. You will work in a fast moving environment to solve business problems as a member of cross-functional team embedded within a business unit. You will be expected to apply prediction and optimization methodologies to large data sets, and to build scalable model production pipelines, and contribute to the design of automated systems around the company. Key job responsibilities The Applied Scientist will be responsible for developing solutions to better predict and optimize the inbound and outbound volumes/backlogs in Amazon's network world wide. This position requires complex analytical problem-solving, quickly approach large ambiguous problems, apply their technical knowledge to identify opportunities for further research and be able to build automated pipelines. Candidates must thrive in fast-paced environments which encourage collaborative and creative problem solving, be able to measure and estimate risks, constructively critique peer research, and align research focuses with the Amazon's strategic needs. About the team QuBIT Science (Midas team) is revolutionizing data-driven decision-making across Amazon's operations. We specialize in delivering swift, scientifically rigorous solutions that often fall outside the immediate scope of larger central science organizations. Applied scientists at Amazon partner closely with senior management, business stakeholders, scientist and engineers to solve key business problems. You will work in a fast moving environment to solve business problems as a member of cross-functional team embedded within a business unit. You will be expected to apply prediction and optimization methodologies to large data sets, and to build scalable model production pipelines, and contribute to the design of automated systems around the company.
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