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AWS Elastic Compute Cloud (EC2) Capacity Org is looking for an experienced applied optimization expert. This leader will join the Optimization Science Team to design, implement, and scale decision-making algorithms to manage EC2's virtual and physical capacity systems.
EC2 Capacity owns EC2's top-level customer satisfaction metric capacity availability and the forecasting & decision-making systems which drive significant capex investments in server ordering for AWS data centers. Optimization Science is a core team involved in the end-to-end design and implementation of various decision-making systems, which manage the trade-off between capex and capacity availability while matching demand and supply at different planning horizons

The stakeholders and partners include engineering and product management orgs within EC2 as well as the AWS Infrastructure Supply Chain (AIS) organization.
We are seeking an expert with a strong background in mathematical optimization with excellent modeling skills, and expertise in the numerical solution of continuous and discrete problems using exact and and heuristic methods applied to very large-scale problems. Experience with decision-making under uncertainty; e.g., robust or stochastic optimization is an advantage. Candidates at the OR/ML interface, and particularly those who have experience applying ML / Gen AI methods to enhance and improve optimization algorithms or optimization-based decision-making systems, are encouraged to apply

The candidate will apply their knowledge to match the end-customer demand for virtual machines to physical resource supply at horizons ranging from five minutes to 13 years. The variety of problems requires principled mathematical decomposition and a good interface design between inputs and outputs at various horizons. Navigating the ambiguity of design choices across horizons is a critical component of the role.

In a typical project, we analyze large volumes of data, and then develop a prescriptive optimization model with inputs from ML or statistical models and business users. Our solution approaches are validated through simulations and / or production A/B tests. Being successful requires having the scientific breadth to understand the interactions between different phases of a project from data analysis through to production, including resolving issues after rollout.
As a Senior Applied Scientist on the EC2 Optimization Science team, you are critical to the speed and excellence of the end-to-end deliveries of production systems with optimization-based analytical engines

You will be hands-on with the mathematical modeling and implementation, and will also contribute to the design of the engineering system with the scalability, extensibility, maintainability, and correctness of the optimization engine in mind.
You will review approaches by other scientists and engineers in terms of business relevance, technical validity, engineering / science interface, and computational performance. You will mentor and lead junior scientists by example.

Communicating your results to guide the direction of the business and working with software development teams to implement your ideas in code is key to success. You will write technical, and less frequently, business documents that influence engineering investments and business direction. Collaborating with other scientists, software engineers, and product managers, you will develop creative, novel, and data-driven approaches to improve our existing cloud compute offerings and define new ones in a fast-paced and quickly changing environment, improving the experience of our customers and impacting the bottom line of EC2.
About the team
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