Sr. AI Risk Manager , Res-Q

Millions of Selling Partners trust Amazons marketplace to grow their businesses and hundreds of millions of customers depend on us every day. Behind that trust is a network of systems tools and workflows designed to detect and resolve fraud and abuse at scale and our team builds them.

About the Role: We are looking for a Sr. Risk Manager who builds risk assessment and insight frameworks powered by this role you will study incoming escalations not to investigate individual cases but to understand the risk landscape deeply enough to build the models classification systems and analytical tools that transform raw escalation signals into structured actionable intelligence at scale.

You will design and deploy AI-driven risk assessment pipelines that automatically categorize incoming escalations surface emerging abuse patterns and quantify systemic defects across hundreds of thousands of annual contacts. You will use Amazons AI and ML infrastructure to build topic models root cause classifiers and risk scoring frameworks that give investigators program teams and senior leaders the insights they need to act faster and prevent abuse before it scales. You will study investigation workflows to determine where automation replaces manual effort where model-driven augmentation improves human judgment and where new data collection mechanisms are needed to close intelligence gaps.

This role offers the opportunity to shape how Amazon protects seller and customer trust by building the AI-powered risk intelligence layer that sits underneath every escalation decision our organization makes.


Key job responsibilities
Design build and deploy AI-powered risk assessment and classification pipelines that automatically categorize incoming escalations against governed root cause taxonomies enabling systematic defect identification and trend detection across the full escalation portfolio
Mine risk signals from unstructured escalation data to surface emerging abuse patterns systemic enforcement gaps and high-impact defect clusters that inform prevention strategies and program priorities
Support the development of machine learning models using Amazon Bedrock SageMaker and supporting infrastructure to automate root cause classification risk scoring and escalation triage
Study investigation processes and escalation workflows to identify where AI-driven automation augmentation or new data collection mechanisms can replace manual effort improve consistency or generate new risk insights
Develop analytical frameworks and visualization layers that translate model outputs into actionable risk intelligence for Risk Managers program teams and senior leadership
Define instrument and monitor model performance metrics alongside operational metrics to measure classification accuracy automation coverage defect reduction and the business impact of AI-driven risk insights
Partner with engineering product operations and science teams to integrate model outputs into centralized tooling infrastructure ensuring AI-powered risk assessments are surfaced at the right point in escalation workflows
Conduct rigorous analysis on model performance classification drift and false-positive/false-negative trends to drive continuous improvement of risk assessment frameworks
Prepare and present data-rich risk papers that track key risk indicators escalation trends and model-derived insights and influence roadmap prioritization by quantifying where applied science delivers the highest operational return


About the team
The Res-Q team operates as the central command for abuse-related escalations conducting investigations and driving systemic improvements across Amazons global stores. Through our various intake channels we handle the most complex and sensitive cases that require expert judgment and cross-functional coordination.

Why Res-Q
Were building the next generation of risk managementone where AI doesnt just support human decisions but fundamentally transforms how we prevent abuse at scale. We pioneered intelligent risk prevention and never stopped innovatingthats why teams across Amazon trust our solutions to protect customers and sellers worldwide.

Inclusive Team Culture
Here at Res-Q its in our nature to learn and be curious about emerging technologies. Our team fosters a culture of innovation and diverse perspectives that empowers us to experiment with AI tools and automation solutions. Ongoing workshops AI learning experiences and cross-functional collaboration inspire us to never stop pushing the boundaries of whats possible.

Mentorship & Career Growth
Were continuously raising our performance bar as we strive to become the most innovative risk management team at Amazon. Thats why youll find endless knowledge-sharing mentorship opportunities and other career-advancing resources here to help you develop.

Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home theres nothing we cant achieve in building intelligent risk solutions.


- 5 years of operations risk fraud investigations industry experience
- Proficiency with SQL and Python for data analysis modeling or automation

- Experience developing operational processes and technologies
- Experience in written and verbal communication skills to communicate with technical and non-technical audiences including senior leadership
- Experience in machine learning data mining information retrieval statistics or natural language processing
- Experience building or deploying machine learning models classification systems or topic modeling pipelines in an operational or risk management context
- Hands-on experience with Amazon Bedrock Amazon SageMaker and Amazon QuickSight (Quick) for model development deployment and analytics

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process including support for the interview or onboarding process please visit for more information. If the country/region youre applying in isnt listed please contact your Recruiting Partner.