Overview:
The Seller Lifecycle Management (SLM) team within Selling Partner Identity Verification (SPIV) focuses on knowing who we're doing business with at every stage of the selling lifecycle, from registering to sell in Amazon's store through any significant changes that impact selling accounts. The Program Manager will be responsible for ensuring that every selling account is operated by a verified and authenticated identity at every stage of the selling lifecycle. They are responsible for independently executing the overall program strategy across four major verticals in the organization: 1) strengthening identity change detection and preventing identity fraud 2) periodic re-verification of selling accounts across the lifecycle 3) applying selling account capability restrictions on bad actors 4) Driving process improvements on how we detect sellers

Key job responsibilities
The SLM team is seeking an exceptional Risk Specialist to focus on identifying and disrupting sophisticated tax fraud schemes across Amazon's marketplace. This role sits at the critical intersection of tax compliance, fraudulent identity detection, and product innovation, with direct visibility to senior leadership as we transform Amazon's approach from reactive compliance to proactive risk intelligence.

Own end-to-end fraud investigations from initial detection through enforcement action, with explicit focus on extracting generalizable patterns that can be automated

Analyze every investigation through the lens of "how do we prevent this at scale?"—identifying the technical controls, data signals, and model features that would have caught this earlier

Prepare detailed investigation reports that include both case-specific findings and broader pattern insights with specific recommendations for Product/Tech/Science teams

Work across investigators to establish tight feedback loops: weekly syncs with Science teams on model performance, bi-weekly reviews with Product on prevention features, monthly deep-dives with Tax on policy implications

Lead incident response for high-impact fraud events or escalations, coordinating cross-functional teams to contain exposure and rapidly deploy preventive measures

Key job responsibilities
Key job responsibilities
The SLM team is seeking an exceptional Risk Specialist to focus on identifying and disrupting sophisticated tax fraud schemes across Amazon's marketplace. This role sits at the critical intersection of tax compliance, fraudulent identity detection, and product innovation, with direct visibility to senior leadership as we transform Amazon's approach from reactive compliance to proactive risk intelligence.

Own end-to-end fraud investigations from initial detection through enforcement action, with explicit focus on extracting generalizable patterns that can be automated

Analyze every investigation through the lens of "how do we prevent this at scale?"—identifying the technical controls, data signals, and model features that would have caught this earlier

Prepare detailed investigation reports that include both case-specific findings and broader pattern insights with specific recommendations for Product/Tech/Science teams

Work across investigators to establish tight feedback loops: weekly syncs with Science teams on model performance, bi-weekly reviews with Product on prevention features, monthly deep-dives with Tax on policy implications

Lead incident response for high-impact fraud events or escalations, coordinating cross-functional teams to contain exposure and rapidly deploy preventive measures

A day in the life
You will part of a team of investigators and analysts to detect, investigate, and prevent tax and fraud perpetrated by bad actors attempting to exploit marketplace facilitator models.

Identify emerging abuse patterns at their earliest stages—detecting signals before they scale into significant damage to Amazon or our customers

Own investigations into sophisticated tax evasion schemes including VAT fraud, shell company abuse, customs manipulation, and identity misrepresentation

Continuously contribute to the refinement of detection logic by feeding investigation learnings back to Science teams, ensuring models evolve faster than fraudster tactics

Champion a culture of continuous improvement where every investigation generates actionable intelligence that strengthens prevention and detection systems

Work with investigators on the team to build case study repositories and pattern libraries that enable rapid knowledge transfer across teams and accelerate response to new fraud vectors