Senior Applied Scientist , EC2 Optimization Science

About the role

The Senior Applied Scientist on the EC2 Optimization Science team will design, implement, and scale decision-making algorithms to manage EC2’s virtual and physical capacity systems. This includes designing and implementing optimization models to match customer demand for virtual machines to physical resource supply at various planning horizons, ranging from five minutes to 13 years.

Responsibilities

• Develop and optimize mathematical models to forecast and manage EC2’s capacity availability and capex investments.
• Apply robust or stochastic optimization techniques to handle decision-making under uncertainty.
• Collaborate with engineering and product management teams to design scalable, maintainable, and correct optimization engines.
• Review and mentor junior scientists, contributing to the scientific and technical aspects of projects.
• Communicate results to guide business decisions and collaborate with software development teams to implement solutions.
• Develop creative, data-driven approaches to improve cloud compute offerings and define new ones in a rapidly evolving environment.

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 applications.
• In-depth knowledge of optimization mathematics, databases, and continuous/discrete optimization methods.
• Experience in traditional programming languages and mathematical solver interfaces.
• Good writing skills for documenting models and analyses and presenting business cases.

Qualifications

• Knowledge of optimization mathematics such as linear programming and nonlinear optimization.
• Knowledge of databases (querying and analyzing) such as SQL, MYSQL, and ETL Manager.
• Experience in quantitative data analysis and statistics.
• Machine learning with applications to optimization.
• Experience in decision-making under uncertainty; e.g., robust or stochastic optimization.

Skills

• Strong background in mathematical optimization with excellent modeling skills.
• Expertise in the numerical solution of continuous and discrete problems using exact and heuristic methods.
• Experience with decision-making under uncertainty; e.g., robust or stochastic optimization.
• Experience applying ML/Gen AI methods to enhance and improve optimization algorithms or optimization-based decision-making systems.

Benefits

Final compensation will be determined based on factors including experience, qualifications, and location. Amazon offers comprehensive benefits including health insurance, retirement plans, paid time off, and parental leave.

Pay

Base salary range: $167,100.00 - $226,100.00 USD annually

Schedule

Full-time position