Description

Amazon is looking for an Applied Scientist to help build the next generation of sourcing and vendor experience systems. The Optimal Sourcing Systems (OSS) owns the optimization of inventory sourcing and the orchestration of inbound flows from vendors worldwide. We source inventory from thousands of vendors for millions of products globally while orchestrating the inbound flow for billions of units. Our goals are to increase reliable access to supply, improve supply chain-driven vendor experience, and reduce end-to-end supply chain costs, all in service of maximizing Long-Term Free Cash Flow (LTFCF) for Amazon.

As an Applied Scientist, you will work with software engineers, product managers, and business teams to understand the business problems and requirements, distill that understanding to crisply define the problem, and design and develop innovative solutions to address them. Our team is highly cross-functional and employs a wide array of scientific tools and techniques to solve key challenges, including optimization, causal inference, and machine learning/deep learning. Some critical research areas in our space include modeling buying decisions under high uncertainty, vendors' behavior and incentives, supply risk and enhancing visibility and reliability of inbound signals.

Key job responsibilities

You will be a science tech leader for the team. As a Applied Scientist you will:

• Set the scientific strategic vision for the team. You - - lead the decomposition of problems and development of roadmaps to execute on it.

• Set an example for other scientists with exemplary scientific analyses; maintainable, extensible, and well-tested code; and simple, intuitive, and effective solutions.

• Influence team business and engineering strategies.

• Exercise sound judgment to prioritize between short-term vs. long-term and business vs. technology needs.

• Communicate clearly and effectively with stakeholders to drive alignment and build consensus on key initiatives.

• Foster collaborations between scientists across Amazon researching similar or related problems.

• Actively engage in the development of others, both within and outside the team.

• Engage with the broader scientific community through presentations, publications, and patents.

Basic Qualifications

• 3+ years of building machine learning models for business application experience

• PhD, or Master's degree

• Experience programming in Java, C++, Python or related language

• Experience with deep learning methods and machine learning

Preferred Qualifications

• Have successful experience of applying hybrid techniques in the space of Machine Learning and Operations Research

• Have experience working with simulation systems

• Excellent written and verbal communication skills with technical and business people; ability to speak at a level appropriate for the audience.

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 $136,000/year in our lowest geographic market up to $222,200/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.