Applied Scientist II: Fraud Detection & ML Solutions

Amazon; Seattle (WA); On-site; USD 143,000 - 193,000; Full time

Job summary

Amazon.com Services LLC in Seattle, WA is seeking a highly qualified Applied Scientist to tackle fraud, theft, and organized crime using ML and data-driven strategies. You will own business challenges from day one and influence decisions with data, impacting millions of items and inventory across our global supply chain.

The role emphasizes building fraud detection models, researching methods to detect bad actors, and working with teams to scale solutions in a fast-paced environment.

Qualifications

• 3 years of building models for business applications 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 algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing.

Responsibilities

• Own KPIs that measure theft/fraud management performance and efficiencies.
• Detect and automate theft, fraud MOs.
• Detect organized crime rings and bad actor clusters.
• Perform end to end evaluation of operational defects, system gaps, and scaling challenges.
• Contribute to the overall fraud management and product development strategies.
• Present key learnings and vision to stakeholders and leadership.
• Integrate ML detection models via software applications

Skills

Model building for business apps

Python programming

Java programming

C programming

Education

PhD or Master’s in CS/CE/ML

Tools

Python

Java

C

Job description

Amazon.com Services LLC in Seattle, WA is seeking a highly qualified Applied Scientist to tackle fraud, theft, and organized crime using ML and data-driven strategies. You will own business challenges from day one and influence decisions with data, impacting millions of items and inventory across our global supply chain.

The role emphasizes building fraud detection models, researching methods to detect bad actors, and working with teams to scale solutions in a fast-paced environment.