Fraud Investigator, Risk Manager II, Fraud Investigations, Recovery & Enforcement (FIRE)

Amazon is seeking a data-driven, technically curious, and results-oriented Senior Fraud Investigator to join the Fraud Investigations, Recovery & Enforcement (FIRE) Digital Fraud team.

This team is responsible for detecting, preventing, and investigating fraudulent vendor and creator activity across Amazon's digital businesses, leveraging advanced fraud detection technologies including graph-based data clustering, machine learning risk scoring models, and automated application screening rules.

The Digital Fraud team operates within Finance Operations and uses transactional signals such as invoices, payments, account attributes, and behavioral patterns as indicators of theft, fraud, waste, or abuse.

We are looking for a strong risk analyst with a proven track record of investigating vendor fraud in procure-to-pay lifecycles and prototyping detection logic to catch fraud the moment it enters the financial system.

The ideal candidate will take ownership of end-to-end fraud investigations, build and test prototype detection rules (using SQL, Python, or AI-assisted tools), contribute to AI/ML-assisted workflows, and translate investigative findings into scalable automation.

This individual thrives in a fast-paced, ambiguous environment, has strong large set data analysis and auditing skills, and is passionate about inventing new ways to detect fraud, ensuring Amazon's Finance Operations stays ahead of evolving schemes while working collaboratively with stakeholders, businesses, and technology teams.

Key job responsibilities

• Execute complex fraud investigations, deep dives, and special projects end-to-end identifying root causes, documenting findings with clear rationale, preserving evidence, and communicating recommendations to management and cross-functional stakeholders

• Partner with technical, legal, business owners and product managers to drive product creation, technical enhancements, process improvements and case resolution

• Demonstrate effective writing through clear and concise narrative creation that documents complex fraud investigations, new program ideas, and periodic senior leadership updates

• Prototype and test fraud detection logic using SQL, Python, or AI-assisted tools to identify new patterns and validate detection hypotheses before escalating for production deployment

• Identify emerging fraud schemes and modus operandi, provide feedback on detection rule performance (true and false positive rates), and contribute to the tuning of fraud detection models through data labeling and investigator feedback

• Query and analyze large datasets using SQL and visualization tools (e.g., Power BI, QuickSight) to validate detection outputs and support investigative analysis

• Own the development and maintenance of dashboards, metrics, and reports that track program KPIs (loss avoidance, rejection rates, detection volumes, false positive rates)

• Contribute to building and updating SOPs, propose process improvements that increase efficiency or detection accuracy, and maintain productivity targets within defined SLAs

About the team

Our mission is to detect and mitigate fraud across Amazon's digital ecosystem by investigating high-risk accounts flagged by advanced machine learning models and fraud detection rules. We continuously innovate by developing prototypes, refining detection algorithms, and reducing false positives, ensuring legitimate businesses thrive while bad actors are stopped.

The individual will collaborate with technology teams, policy owners, and cross-functional stakeholders to close investigations, invent new detection mechanisms, and drive program metrics.

• Bachelor's degree or equivalent
• Experience with reporting and Data Visualization tools such as Quick Sight / Tableau / Power BI or other BI packages
• Experience with Microsoft Office products at an advanced level and SQL
• 5+ years of managing complex investigations end to end, legal, governance, audit, risk/loss prevention, or equivalent experience
• 3+ years of program requirements definition and data and metrics leveraging to drive improvements experience
• Knowledge of machine learning concepts and their application to reasoning and problem-solving
• Experience prototyping fraud or anomaly detection rules using SQL, Python, scripting tools, or generative AI, translating investigative insights into testable detection mechanisms

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