collaborate with data scientists, fraud strategists, and business stakeholders to support and enhance real-time fraud detection strategies
uncover patterns related to account takeover, scam activity, and coordinated fraud attacks using advanced analytics and large-scale data
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

Requirements

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

Benefits

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Applicant Tracking System Keywords

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Hard Skills & Tools

PythonSQLdata analysisautomationmodelinganomaly detectionbehavioral analyticsfraud detection methodologiesstatistical analysisdata visualization

Soft Skills

communicationcollaborationanalytical thinkingproblem-solvinginfluencing stakeholders