Amazon’s Account Integrity team (AIT) within the Selling Partner Support organization is looking for a passionate, results-oriented Risk Mining Analyst to leverage data, detect anomalies and prevent bad actors for harming our genuine customers. The incumbent will lead one of the core verticals of Risk Mining with AIT. The AIT team owns designing and building high performance software systems using machine learning that identify and prevent fraudulent activity and maintain high trust levels with our customers. Fraud and abuse prevention is a real-money game where our software and analytics teams strive to outsmart those who attempt to defraud Amazon and our customers. As a Risk Analyst for Risk Mining in the Account Integrity group, you will be responsible for analyzing large data sets in partnership with product managers, scientists, and engineers, drive root cause analysis of ever changing bad actor behaviors and deploy rules that will prevent bad actors from gaining access to Amazon's platform.
Key job responsibilities
- Maintain, and improve data sets, pipelines and reporting to track and manage important KPIs and goals in the Account Integrity space
- Analyze customer trends to identify patterns, develop attributes that clearly articulate those pattern and incorporate those attributes into static rules for automated deployment in our account creation pipeline.
- Operate as an on-call during rotation weeks to respond to high severity events impacting Amazon
- Apply your expertise in quantitative analysis, data visualization and data-mining to design alarm systems, derive actionable insights and guide product strategy for stakeholders and leadership
- Support the the design, development, and maintenance of ongoing metrics, reports, analyses, dashboards, etc. within SQL and other BI tools
- Support cross-functional teams on the day-to-day execution of projects and initiatives
- Communicate analysis and insights to stakeholders and business leaders, both verbally and in writing
A day in the life
- Review instances of bad actor account activity and create rules for short term and mechanisms for long term to stop them
- Report on fraud and abuse trends and leverage that information to support product development inputs
- Create and maintain dashboards that detect and inform stakeholders (such as downstream payment and abuse product owners) on how to stop fraud and abuse via early stage detection of bad actor behavior
- Participate and lead process improvement initiatives across account integrity teams
- Develop rules to mitigate bad actor activity
- Log in during on-call week to respond to any high severity events and if needed develop short term rules to mitigate impact
About the team Amazon's Account Integrity Team (AIT) sits within the Account Integrity Machine Learning organization, where we protect customers by detecting and disrupting organized bad actor activity — including account takeovers and fraudulent account creation at scale. Our team of Risk Analysts and Risk Mining Specialists works at the intersection of data, machine learning, and fraud prevention, collaborating closely with cross-functional partners to deploy both short-term mitigations and long-term solutions.
This role supports globally distributed teams across Costa Rica, India, and the United States, contributing to continuous, round-the-clock coverage of high-severity events. Team members participate in an on-call rotation to ensure rapid response to emerging threats.
Basic Qualifications
- 2+ years of work in relevant industries such as law, customer service, investigations or project management experience
- Experience working effectively across cross-functional teams and partnering well with people at all levels within an organization
- Bachelor's degree or equivalent, or 4+ years of industry experience
- Experience using SQL databases to manage and analyze large data sets
- Experience analyzing data to identify patterns, trends, or anomalies and communicating findings to stakeholders
Preferred Qualifications
- Experience with Lean or Six Sigma analytical techniques
- Experience including, building and maintaining data flows and pipelines
- Experience identifying patterns of coordinated or organized abuse (e.g., account takeovers, fraudulent account creation, synthetic identity fraud)
- Exposure to rule-based detection systems or policy enforcement tooling (e.g., writing, testing, or evaluating detection rules)
- Familiarity with machine learning concepts as applied to fraud or anomaly detection (no coding required)
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