Amazon Development Center U.S., Inc.
Technology
SeniorAppliedScientist,EC2OptimizationScience
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“Senior Applied Scientist, EC2 Optimization Science at Amazon Development Center U.S., Inc.. Skills: Mathematical optimization, Machine learning, Algorithm development. Design decision-making algorithms. Implement decision-making algorithms”
Industry & Context.
Mathematical decomposition; Resolving issues
What They're Looking For.
Must Have
Master's degree and 4+ years experience, Knowledge of optimization mathematics, Knowledge of databases (querying and analyzing), In-depth knowledge of optimization methods, Experience in prototyping and developing software, Good writing skills
Nice to Have
PhD preferred, Knowledge of quantitative data analysis, Machine learning with applications to optimization, Experience in decision-making under robust or stochastic optimization
What You'll Do.
Design decision-making algorithms
Implement decision-making algorithms
Scale decision-making algorithms
Manage virtual capacity systems
Manage physical capacity systems
Develop prescriptive optimization models
Validate solution approaches
Analyze large volumes of data
Develop optimization models
Match end-customer demand
Match physical resource supply
Decompose mathematical problems
Design interface between inputs and outputs
Navigate ambiguity of design choices
Understand project phases
Resolve issues after rollout
Deliver production systems
Design engineering systems
Review approaches by scientists
Review approaches by engineers
Mentor junior scientists
Lead junior scientists
Guide business direction
Work with software development teams
Implement ideas in code
Write technical documents
Write business documents
Develop creative approaches
Develop novel approaches
Develop data-driven approaches
Improve cloud compute offerings
Define new cloud offerings
How You'll Work.
Team & Collaboration
Engineering and product management orgs; AWS Infrastructure Supply Chain; Other scientists; Software engineers; Product managers
Communication Scope
Presenting business cases; Communicating results
Full Job Description
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 o
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