{"schemaVersion":"jobsearcher.job.v1","id":"641d2fb21464efbbc6c15bc1","url":"https://jobsearcher.com/jobs/641d2fb21464efbbc6c15bc1","canonicalUrl":"https://jobsearcher.com/jobs/641d2fb21464efbbc6c15bc1","title":"Senior Applied Scientist , EC2 Optimization Science","description":"Senior Applied Scientist, EC2 Optimization Science\nJob ID: 10391414 | Amazon Development Center U.S., Inc.\n\nAWS 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.\n\nEC2 Capacity owns the top‑level customer satisfaction metric of capacity availability and the forecasting & decision‑making systems that 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, managing the trade‑off between capex and capacity availability while matching demand and supply across multiple horizons. Stakeholders and partners include engineering and product management orgs within EC2 and the AWS Infrastructure Supply Chain (AIS) organization.\n\nWe seek an expert with a strong background in mathematical optimization, excellent modeling skills, and 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 (robust or stochastic optimisation) is an advantage. Candidates at the OR/ML interface, especially those who have applied ML / Gen AI methods to enhance optimisation algorithms or optimisation‑based decision‑making systems, are encouraged to apply. The role requires understanding interactions across the entire project lifecycle—from data analysis through to production—and resolving issues after rollout.\n\nResponsibilities\n\nDesign, implement, and scale decision‑making algorithms for EC2 capacity management.\n\nApply mathematical optimisation techniques, including linear programming, nonlinear optimisation, continuous and discrete methods, exact and heuristic approaches to large‑scale problems.\n\nIncorporate decision‑making under uncertainty (robust or stochastic optimisation) and ML / Gen AI methods to enhance optimisation models.\n\nAnalyse large volumes of data, develop prescriptive optimisation models with inputs from ML or statistical models, and validate solutions through simulations and A/B tests.\n\nTranslate end‑user data insights into production‑ready optimisation engines, ensuring scalability, extensibility, maintainability, and correctness.\n\nReview approaches of other scientists and engineers, providing feedback on business relevance, technical validity, interface design, and computational performance.\n\nMentor and lead junior scientists, communicate results to business and engineering teams, and write technical and business documents that influence engineering investments and business direction.\n\nBasic Qualifications\n\nPhD in operations research, applied mathematics, theoretical computer science, or equivalent; OR Master’s degree and 4+ years of experience building machine learning models or developing business‑application algorithms.\n\nKnowledge of optimisation mathematics such as linear programming and nonlinear optimisation.\n\nKnowledge of databases (querying and analysing) such as SQL, MySQL, and ETL Manager, and experience working with large data sets.\n\nIn‑depth knowledge of continuous and discrete optimisation methods with expertise in tools and the latest technology (e.g., CPLEX, Gurobi, XPRESS).\n\nExperience prototyping and developing software in traditional programming languages (C++, Java, Python, Julia) using mathematical solver interfaces.\n\nGood writing skills to document models and analyses and to present business cases with results/conclusions that influence important decisions.\n\nPreferred Qualifications\n\nKnowledge of quantitative data analysis and statistics.\n\nMachine learning applications to optimisation.\n\nExperience in decision‑making under uncertainty; e.g., using robust or stochastic optimisation.\n\nThe base salary range for this position is $167,100.00 – $226,100.00 USD annually. Your Amazon package will include sign‑on payments and restricted stock units (RSUs). Final compensation will be determined based on experience, qualifications, and location. Amazon also offers comprehensive benefits, including health insurance (medical, dental, vision, prescription, Basic Life & AD&D, 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 https://amazon.jobs/en/benefits .\n\nAmazon 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, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information.\n\n#J-18808-Ljbffr","company":"Amazon","rawCompany":"amazon","city":"Millbrae","state":"CA","isRemote":false,"isActive":true,"createdAt":"2026-06-17T04:11:55.346Z","occupations":[{"code":"15-2031.00","title":"Operations Research Analysts","slug":"operations-research-analysts"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-2099.00","title":"Mathematical Science Occupations, All Other","slug":"mathematical-science-occupations-all-other"}],"industries":[{"code":"541690","title":"Other Scientific and Technical Consulting Services","slug":"other-scientific-and-technical-consulting-services"},{"code":"541715","title":"Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)","slug":"research-and-development-in-the-physical-engineering-and-life-sciences-except-nanotechnology-and-biotechnology"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Senior Applied Scientist , EC2 Optimization Science","description":"Senior Applied Scientist, EC2 Optimization Science\nJob ID: 10391414 | Amazon Development Center U.S., Inc.\n\nAWS 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.\n\nEC2 Capacity owns the top‑level customer satisfaction metric of capacity availability and the forecasting & decision‑making systems that 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, managing the trade‑off between capex and capacity availability while matching demand and supply across multiple horizons. Stakeholders and partners include engineering and product management orgs within EC2 and the AWS Infrastructure Supply Chain (AIS) organization.\n\nWe seek an expert with a strong background in mathematical optimization, excellent modeling skills, and 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 (robust or stochastic optimisation) is an advantage. Candidates at the OR/ML interface, especially those who have applied ML / Gen AI methods to enhance optimisation algorithms or optimisation‑based decision‑making systems, are encouraged to apply. The role requires understanding interactions across the entire project lifecycle—from data analysis through to production—and resolving issues after rollout.\n\nResponsibilities\n\nDesign, implement, and scale decision‑making algorithms for EC2 capacity management.\n\nApply mathematical optimisation techniques, including linear programming, nonlinear optimisation, continuous and discrete methods, exact and heuristic approaches to large‑scale problems.\n\nIncorporate decision‑making under uncertainty (robust or stochastic optimisation) and ML / Gen AI methods to enhance optimisation models.\n\nAnalyse large volumes of data, develop prescriptive optimisation models with inputs from ML or statistical models, and validate solutions through simulations and A/B tests.\n\nTranslate end‑user data insights into production‑ready optimisation engines, ensuring scalability, extensibility, maintainability, and correctness.\n\nReview approaches of other scientists and engineers, providing feedback on business relevance, technical validity, interface design, and computational performance.\n\nMentor and lead junior scientists, communicate results to business and engineering teams, and write technical and business documents that influence engineering investments and business direction.\n\nBasic Qualifications\n\nPhD in operations research, applied mathematics, theoretical computer science, or equivalent; OR Master’s degree and 4+ years of experience building machine learning models or developing business‑application algorithms.\n\nKnowledge of optimisation mathematics such as linear programming and nonlinear optimisation.\n\nKnowledge of databases (querying and analysing) such as SQL, MySQL, and ETL Manager, and experience working with large data sets.\n\nIn‑depth knowledge of continuous and discrete optimisation methods with expertise in tools and the latest technology (e.g., CPLEX, Gurobi, XPRESS).\n\nExperience prototyping and developing software in traditional programming languages (C++, Java, Python, Julia) using mathematical solver interfaces.\n\nGood writing skills to document models and analyses and to present business cases with results/conclusions that influence important decisions.\n\nPreferred Qualifications\n\nKnowledge of quantitative data analysis and statistics.\n\nMachine learning applications to optimisation.\n\nExperience in decision‑making under uncertainty; e.g., using robust or stochastic optimisation.\n\nThe base salary range for this position is $167,100.00 – $226,100.00 USD annually. Your Amazon package will include sign‑on payments and restricted stock units (RSUs). Final compensation will be determined based on experience, qualifications, and location. Amazon also offers comprehensive benefits, including health insurance (medical, dental, vision, prescription, Basic Life & AD&D, 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 https://amazon.jobs/en/benefits .\n\nAmazon 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, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information.\n\n#J-18808-Ljbffr","datePosted":"2026-06-17T04:11:55.346Z","dateModified":"2026-06-17T04:11:55.346Z","hiringOrganization":{"@type":"Organization","name":"Amazon","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"641d2fb21464efbbc6c15bc1"},"url":"https://jobsearcher.com/jobs/641d2fb21464efbbc6c15bc1"}}