Amazon Applied Scientist II Cyber Fraud Prevention 

United States, Washington, Seattle 

861945808

Key job responsibilities
* Design and develop novel machine learning models to detect abuse patterns across logistics systems, focusing on anomaly detection and adaptive learning approaches
* Analyze large-scale logistics data to identify emerging abuse vectors and quantify their business impact
* Partner with security teams to translate abuse scenarios into model features and detection rules
* Work closely with engineering teams to implement real-time detection systems
* Collaborate with operations teams to validate findings and refine models based on ground truth
* Research and implement innovative approaches for connecting signals across disparate systems
* Establish scalable processes for model development, validation and deployment
* Provide clear insights to leadership on abuse trends and model performanceA day in the life
About the team
Diverse Experiences
Amazon Security values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
Work/Life BalanceTraining and Career growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, training, and other career-advancing resources here to help you develop into a better-rounded professional.

- 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 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 solving business problems through machine learning, data mining and statistical algorithms

- Experience applying theoretical models in an applied environment
- Experience in investigating, designing, prototyping, and delivering new and innovative system solutions
- Experience with abuse/fraud detection systems.
- Demonstrated ability to work in ambiguous problem spaces
- Track record of end-to-end ML project ownership, Strong communication skills and ability to influence cross-functional partners