Sr. Applied Scientist, Selling Partner Trust

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.

Key job responsibilities

• Use machine learning and statistical techniques to create scalable abuse detection solutions that identify fraudulent seller behavior, account takeovers, 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

About the team
You'll be working closely with business partners 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.

- 4+ years of building machine learning models for business application experience
- PhD, or Master's degree and 3+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
- 4+ years of applied research experience

- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, 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 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 This position will remain posted until filled. Applicants should apply via our internal or external career site.

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