Amazon Senior Applied Scientist EC2 Optimization Science 

United States, Washington, Seattle 

967726582

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. 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.**Basic Qualifications**
- PhD in Operations Research, Applied Mathematics, Computer Science, Statistics, or a related field. A PhD can be replaced by a master's degree in the same fields and four years of relevant academic and / or industry research experience.
- At least 3 years of academic and / or industry experience after the PhD degree in solving large-scale optimization problems.
- 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).
- Ability to implement models and tools through the use of high-level modeling languages (e.g., AMPL, Mosel, R, Matlab).
- Experience in prototyping and developing software in traditional programming languages (e.g., C++, Java, Python, Julia) using mathematical solver interfaces.
- Familiarity with SQL and experience with very large-scale data. The ability to manipulate data by writing scripts (Python, Perl, Ruby) is a plus.
**Preferred Qualifications**
These are not required, but are a plus:
- Knowledge and experience in statistical analysis and machine learning.
- Publications in refereed academic journals.
- Previous work in cloud computing.

- PhD in operations research, applied mathematics, theoretical computer science, or equivalent, or Master's degree and 7+ years of building machine learning models or developing algorithms for business application experience

- Statistical analysis
- Machine learning