Applied Scientist, Amazon Selling Partner Trust & Store Integrity Science

Amazon•Seattle, WA

About The Position

Are you passionate about applying machine learning and advanced statistical techniques to protect one of the world's largest online marketplaces? Do you want to be at the forefront of developing innovative solutions that safeguard Amazon's customers and legitimate sellers while ensuring a fair and trusted shopping experience? Do you thrive in a collaborative environment where diverse perspectives drive breakthrough solutions? If yes, we invite you to join the Amazon Risk Intelligence Science Team. We're seeking an exceptional scientist who can revolutionize how we protect our marketplace through intelligent automation. As a key member of our team, you'll develop and deploy state-of-the-art machine learning systems that analyze millions of seller interactions daily, ensuring the integrity and trustworthiness of Amazon's marketplace while scaling our operations to new heights. Your work will directly impact the safety and security of the shopping experience for hundreds of millions of customers worldwide, while supporting the growth of honest entrepreneurs and businesses. You'll be working closely with business partners, science and engineering teams to create end-to-end scalable machine learning solutions that address real-world problems. You will build scalable, efficient, and automated processes for large-scale data analyses, model development, model validation, and model implementation. You will also be providing clear and compelling reports for your solutions and contributing to the ongoing innovation and knowledge-sharing that are central to the team's success.

Requirements

PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
3+ years of building machine learning models for business application experience
Experience programming in Java, C++, Python or related language
Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
Experience in investigating, designing, prototyping, and delivering new and innovative system solutions
Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
Experience in patents or publications at top-tier peer-reviewed conferences or journals

Responsibilities

Use machine learning and statistical techniques to create scalable abuse detection solutions that identify fraudulent seller behavior, identity change, and marketplace manipulation schemes
Innovate with the latest GenAI technology to build highly automated solutions for efficient seller verification, transaction monitoring, and risk assessment
Design, develop and deploy end-to-end machine learning solutions in the Amazon production environment to prevent and detect sophisticated abuse patterns across the marketplace
Learn, explore and experiment with the latest machine learning advancements to protect customer trust and maintain marketplace integrity while supporting legitimate selling partners
Collaborate with cross-functional teams to develop comprehensive risk models that can adapt to evolving abuse patterns and emerging threats

Benefits

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 $223,400/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.

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