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Description

The Stores Economics and Science (SEAS) team uses Economics, Statistics, Operations Research and Machine Learning to understand and design the complex economy of Amazon’s network of buyers and sellers. We are an interdisciplinary team, committed to use of cutting edge technology and leveraging the strengths of engineers and scientists to build solutions for some of the toughest business problems at Amazon.

We are looking for an outstanding Principal Applied Scientist who can invent novel approaches to previously unseen and intrinsically hard supply chain and logistics problems. We are looking for creative scientific experts who can combine a strong optimization toolbox with a desire to learn from others, and who know how to execute and deliver on big ideas. Experience in Operations Research, Optimization, Control Theory is essential, and you should be familiar with modern tools for data science and business analysis.

Key job responsibilities

This position requires drive and self-motivation, superior analytical thinking, data-driven disposition, application of technical knowledge to a business context, effective collaboration with fellow scientists, software development engineers, and product managers, effective communication of technical designs to technical and non-technical audiences, and close partnership with many stakeholders from operations, finance, IT, and business leadership.

Key job responsibilities:

• Seek to understand in depth the end to end eCommerce supply chain and identify areas of opportunities to grow our business using science solutions.

• Apply state of the art optimization tools to intrinsically complex problems.

• Contribute to the science strategy for SEAS Supply Chain and Logistics team.

• Drive alignment across organizations to achieve business goals.

• Lead/guide scientists and engineers across teams to develop, test, launch and improve of science models designed to optimize inventory value and customer experience.

• Be responsible for communicating our innovations to the broader internal & external scientific community and business leadership.

• Mentor and guide the applied scientists in our organization and hold us to a high standard of technical rigor and excellence in Operations Research.

Basic Qualifications

• PhD in Operations Research, Industrial Engineering, Control Theory, Applied Math, Computer Science or equivalent.

• Publications at top-tier peer-reviewed conferences or journals.

• Fluent in Java, C++, Python or related language

• Hands-on experience in building optimization models for business applications

• 10+ years of relevant, broad research experience after PhD

Preferred Qualifications

• Deep expertise in Distributed Optimization, Stochastic Optimization, Dynamic Programming, or Reinforcement Learning.

• Experience in professional software development

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. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $179,000/year in our lowest geographic market up to $309,400/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.