Analyze complex datasets to identify risk signals related to fraudulent debit card transactions.
Use Python, SQL, and analytical tools to analyze large, complex datasets and extract actionable insights.
Implement fraud detection strategies in a real-time fraud decisioning engine.
Support anomaly detection efforts to identify coordinated fraud attacks and organized fraud rings.
Communicate insights clearly and effectively to influence stakeholders and drive product changes.
Collaborate across business units to drive data-informed strategies for fraud detection.
Contribute to the continuous improvement of the fraud detection program, through rule and model refinement, fraud hunting, and data discovery.
Requirements
Bachelor's with 5 plus years or Master's with 3 plus years of experience in data analytics, preferably in fraud detection, cybersecurity, or risk domains.
Strong proficiency in Python and SQL for data analysis and automation.
Experience working with large-scale datasets and analytical platforms.
Skilled in using analytics tools (e.g., dashboards, statistical packages, data visualization platforms) to perform advanced analytics and uncover actionable insights.
Familiarity with payment card fraud detection and risk management.
Ability to translate complex data into clear, actionable insights.
Strong communication and collaboration skills to work across teams and influence decisions.
Experience in anomaly detection or fraud analytics is a plus.