Applied Scientist, Buyer Abuse Prevention ML

Amazon
San Diego, CA, USA2026-04-09ONSITE

About the job

Amazon.com’s Buyer Risk Prevention's (BRP) mission is to make Amazon the safest and most trusted place worldwide to transact online. BRP safeguards every financial transaction across all Amazon sites. As such, BRP designs and builds the software systems, risk models, and operational processes that minimize risk and maximize trust in Amazon.com. The BRP organization is looking for an Applied Scientist for the Buyer Abuse team, whose mission is to combine advanced analytics with investigator insight to create mechanisms to proactively and reactively reduce the impact of abuse across Amazon.

Responsibilities

Invent, implement, and deploy state of the art machine learning algorithms and systems

Build prototypes and explore conceptually new solutions

Define and conduct experiments to validate/reject hypotheses, and communicate insights and recommendations to Product and Tech teams

Take ownership of how ML solutions impact Amazon resources and Customer experience

Develop efficient data querying infrastructure for both offline and online use cases

Collaborate with cross-functional teams from multidisciplinary science, engineering and business backgrounds to enhance current automation processes

Learn and understand a broad range of Amazon’s data resources and know when, how, and which to use and which not to use.

Research and implement novel machine learning and statistical approaches

Maintain technical document and communicate results to diverse audiences with effective writing, visualizations, and presentations

Qualifications

Minimum

3+ years of building models for business application experience

PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience

Experience in patents or publications at top-tier peer-reviewed conferences or journals

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

Preferred

Experience using Unix/Linux

Experience in professional software development