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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. 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.

• Track record of delivering analytical solutions with business impact.

• In-depth knowledge of exact, approximation algorithms, and heuristic methods for solving difficult optimization problems like resource allocation, vehicle routing, network design.

• 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.

• 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

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.

Basic Qualifications

• 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

Preferred Qualifications

• Statistical analysis

• Machine learning

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, 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 https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $150,400/year in our lowest geographic market up to $260,000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.