Senior Applied Scientist , EC2 Optimization Science

DescriptionAWS 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 EC2u2019s virtual and physical capacity systems. EC2 Capacity owns EC2u2019s 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 Why AWS Amazon Web Services (AWS) is the worldu2019s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating u2014 thatu2019s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, itu2019s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, thereu2019s nothing we canu2019t achieve in the cloud. Mentorship and Career Growth Weu2019re continuously raising our performance bar as we strive to become Earthu2019s Best Employer. Thatu2019s why youu2019ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasnu2019t followed a traditional path, or includes alternative experiences, donu2019t let it stop you from applying. Basic Qualifications- 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 software in traditional programming languages (e.g., C++, Java, Python, Julia) using mathematical solver interfaces. - Good writing skills to document the models and analyses and for presenting business cases with results/conclusions in order to influence important decisions.Preferred Qualifications- Knowledge of quantitative data analysis and statistics - Machine learning with applications to optimization - Experience in decision-making under uncertainty; e.g., using robust or stochastic optimization. Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit for more information. If the country/region youu2019re applying in isnu2019t listed, please contact your Recruiting Partner. The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at . USA, WA, Seattle - 167,100.00 - 226,100.00 USD annually

Created: 2026-04-16