Senior Applied Scientist , EC2 Optimization Science

## Responsibilities - Design, implement, and scale decision-making algorithms to manage EC2’s virtual and physical capacity systems. - Develop prescriptive optimization models with inputs from ML or statistical models and business users. - Validate solutions through simulations and/or production A/B tests. - Review approaches by other scientists and engineers in terms of business relevance, technical validity, engineering/science interface, and computational performance. - Mentor and lead junior scientists by example. - Communicate results to guide the direction of the business and work with software development teams to implement ideas in code. - Write technical and less frequently business documents that influence engineering investments and business direction. - Collaborate with other scientists, software engineers, and product managers to develop creative, novel, and data-driven approaches to improve cloud compute offerings and define new ones. - Navigate the ambiguity of design choices across different planning horizons. - Apply knowledge to match end-customer demand for virtual machines to physical resource supply at horizons ranging from five minutes to 13 years. - Ensure the scalability, extensibility, maintainability, and correctness of the optimization engine. ## Requirements - PhD in operations research, applied mathematics, theoretical computer science, or equivalent, or Master's degree and 4+ years of building machine learning models or developing algorithms for business application experience. - Knowledge of optimization mathematics such as linear programming and nonlinear optimization. - Knowledge of databases (querying and analyzing) such as SQL, MYSQL, and ETL Manager and working with large data sets. - In-depth knowledge of continuous and discrete optimization methods accompanied by associated expertise in the use of tools and the latest technology (e.g. CPLEX, Gurobi, XPRESS). - Experience in prototyping and developing optimization-based decision-making systems. - Strong background in mathematical optimization with excellent modeling skills. - Expertise in the numerical solution of continuous and discrete problems using exact and heuristic methods applied to very large-scale problems. - Experience with decision-making under uncertainty; e.g., robust or stochastic optimization. ## Nice to Have - Candidates at the OR/ML interface, particularly those with experience applying ML/Gen AI methods to enhance and improve optimization algorithms or optimization-based decision-making systems. - Experience with the end-to-end design and implementation of various decision-making systems. - Experience working with stakeholders and partners including engineering and product management orgs within EC2 as well as the AWS Infrastructure Supply Chain (AIS) organization. ## Benefits - Inclusive Team Culture - Work/Life Balance - Mentorship and Career Growth - Diverse Experiences - Opportunity to work on impactful projects that influence the bottom line of EC2 and improve customer experience.