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The Amazon Fulfillment Technologies (AFT) Science team is looking for an experienced, innovative and exceptional Senior Applied Scientist, with strong Machine Learning, statistical, and analytical skills, to develop critical forecasting models for one of the most complex systems in the world: Amazon's Fulfillment Network. At AFT Science, we design, build and deploy optimization, simulation, and machine learning solutions to power the production systems running at world wide Amazon Fulfillment Centers. We solve a wide range of problems that are encountered in the network, including labor planning and staffing, demand prioritization, pick assignment and scheduling, flow process optimization, and order fulfillment. We are tasked to develop innovative, scalable, and reliable science-driven solutions that are beyond the published state of art in order to run frequently (ranging from every few minutes to every few hours per use case) and continuously in our large scale network. We develop scalable and robust state-of-the-art ML and Optimization driven solutions that involve learning from different data sources and advanced descriptive, diagnostic, predictive prescriptive and cognitive models. With better forecasts, critical algorithms employed at Amazon Fulfillment Network will achieve higher efficiency, improving the customer experience directly. In this role, you will have an opportunity to both develop advanced scientific solutions, and drive critical customer and business impact. You will play a key role to drive end-to-end solutions from understanding our business requirements, exploring a large amount of historical data and ML models, building prototypes and exploring conceptually new solutions, to working with partner teams for prod deployment. You will collaborate closely with scientists, engineering peers as well as business stakeholders.
