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, in partnership with Region Flexibility, is driving an SDO-wide program to diversify our use of AWS regions beyond DUB, IAD, and PDX regions. The objective of the Diversify AWS Region Usage (DARU) program is to mitigate the risk of capacity concentration by encouraging teams to design workloads that are region-flexible, utilize AWS automation such as Flexible Fleets to access multiple capacity pools, and optimize workload placement so SDO efficiently utilizes AWS. This is a strategic, highly visible, multi-year program which spans all Amazon business.
CloudTune is looking for an Applied Scientist to join our forecasting team and support DARU program. The team develops sophisticated algorithms that involve learning from large amounts of past data, such as actual sales, website traffic, merchandising activities, promotions, similar products and product attributes to forecast the demand for our compute infrastructure. These forecasts are used to determine the level of investment in capital expenditures, promotional activity, engineering efficiency projects and determining financial performance.
As an Applied Scientist in CloudTune, you will work with other scientists, software engineers, data engineers, and product managers on a variety of important applied machine learning problems in the area of time series modeling. You will work on statistical problems with a high level of ambiguity. 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 businesses.
Key job responsibilities
• Process and analyze large data sets, mining additional data sources as needed
• Analyze compute scaling metrics to identify business drivers that influence infrastructure expenditures
• Build statistical models and drive scalable solutions for multi-year capacity demand forecasting horizons
• Prototype these models by using high-level modeling languages such as R or Python
• Create, enhance, and maintain technical documentation, and present to other scientists and business leaders
