Research Scientist, CloudTune

Amazon's eCommerce Foundation (eCF) organization provides the core technologies that drive and power Amazon's Stores, Digital, and Other (SDO) businesses. Millions of customer page views and orders per day are enabled by the systems eCF builds from the ground up. CloudTune, within eCF, empowers growth and business agility needs by automatically and efficiently managing AWS capacity and business processes needed to safely meet Amazon's customer demand. CloudTune serves its primary customers, internal software teams, through forecast-driven automation of cost controllership, capacity management, and scaling. We predict expected load, and drive procurement and allocation of AWS capacity for new product launches and high-velocity events like Prime Day and Cyber Monday. CloudTune is looking for a Research Scientist to join our forecasting and optimization team. The team develops sophisticated algorithms that combine machine learning-based demand forecasting with mathematical optimization to solve large-scale capacity planning problems under uncertainty. We work with massive datasets — spanning thousands of availability zone and instance family combinations across global regions — to determine optimal resource allocation strategies that balance infrastructure holding costs against service availability requirements. These models directly inform multi-million dollar capacity investment decisions and drive automated procurement, retention, and release policies across Amazon's compute infrastructure. As a Research Scientist in CloudTune, you will work with other scientists, software engineers, data engineers, and product managers on a variety of important research problems in the areas of stochastic optimization, time series modeling, and operations research. You will formulate capacity planning challenges as constrained optimization problems, develop demand forecasting models that account for asymmetric risk, and design allocation frameworks that minimize costs while maintaining fulfillment guarantees. You will analyze and process large amounts of data, develop new algorithms and improve existing approaches based on statistical models, machine learning algorithms, and big data solutions to automatically scale Amazon's compute infrastructure, optimizing the balance between availability risk and cost efficiency for all of Amazon's businesses. Key job responsibilities - Formulate capacity planning and resource allocation challenges as mathematical optimization problems (linear programming, stochastic optimization, mixed-integer programming) - Develop demand forecasting models using machine learning (XGBoost, quantile regression, deep learning) with risk-aware loss functions tailored to operational objectives - Design and implement safety stock and retention policy optimization frameworks that balance holding costs against fulfillment risk across constrained and unconstrained capacity pools - Process and analyze large-scale operational data (order histories, capacity utilization, availability constraints) to identify patterns and inform model development - Create, enhance, and maintain technical documentation, and present research findings to scientists, engineering teams, and senior leadership