Risk Specialist I, SPIV
Amazon - Hyderabad, Telangana, IND
Posted Apr 10, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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Schedule
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- Weekend work
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Application
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Where they hire
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About this role
Risk Specialist I, SPIV Hyderabad, Telangana, IND 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
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