Amazon’s Account Integrity team (AIT) within the Selling Partner Support organization is looking for a passionate, results-oriented Risk Analyst (similar to Business 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 Team 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. - 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 1. Review instances of bad actor account activity and create rules for short term and mechanisms for long term to stop them 2. Report on fraud and abuse trends and leverage that information to support product development inputs 3. 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 4. Participate and lead process improvement initiatives across account integrity teams 5. Develop rules to mitigate bad actor activity 6. 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 Our team consists of Risk Managers, Risk Analysts, and Risk Specialists roll up under the Account integrity Machine Learning Team. We evaluate instances of organized bad actor account take over and creation of fraudulent accounts on Amazon. We collaborate closely across multiple functions in order to mitigate these attacks through various short and long term solutions. This role will support existing teams that are based out of Costa Rica, India and the US and will enable provision of round the clock coverage of Severity -2 support for AIT. ### Basic qualifications - Bachelor's degree or equivalent - Knowledge of and proficiency in the use of Python scripting language - Experience with SQL and Excel - Experience in data analytics or automation - 1+ years of business analyst, data analyst or similar role experience ### Preferred qualifications - Basic understanding of Machine Learning and Ability to leverage Gen AI tools - Experience handling confidential information 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 https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.