Analyst, Fraud Detection and Analytics
- Analyze large-scale datasets using Python, SQL, and modern analytics tools to identify fraud patterns, trends, and emerging threats.
- Support anomaly detection and behavioral analytics to identify coordinated attacks and organized fraud rings.
- Leverage third-party vendor solutions and build internal tools to assess authentication risk and session-based fraud signals.
- Assist in the development, refinement, and monitoring of fraud detection models, dashboards, and reporting frameworks.
- Translate complex analytical findings into clear, actionable insights to influence stakeholders and drive product and policy changes.
- Collaborate across business and technical teams to deliver data-driven fraud detection strategies.
- 3-5 years of experience in data analytics, preferably within fraud detection, risk management, or cybersecurity.
- Strong proficiency in Python and SQL for data analysis, automation, and modeling.
- Experience working with large, complex datasets and modern data platforms.
- Proficiency with analytics and visualization tools for statistical analysis and reporting.
- Familiarity with fraud detection methodologies, particularly account compromise and scam prevention.
- Strong communication skills with the ability to explain complex data to both technical and non-technical audiences.
- Experience with anomaly detection or behavioral analytics is a plus.
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