Data Analyst, Fraud Data & Analytics

Responsibilities:

• Executes fraud analytics assignments from requirements gathering and data assessment through analysis, implementation support, measurement, and monitoring.
• Develops, tests, and maintains offline fraud detections that generate actionable leads for investigative teams, with guidance on more complex efforts.
• Monitors detection performance using measures such as precision, false-positive rates, alert volumes, loss exposure, and prevented or avoided impact.
• Analyzes account, client, transactional, and case data to identify emerging fraud trends, anomalous behavior, common attributes, and fraud signatures.
• Provides analytical and forensic support for fraud incidents, control gaps, emerging typologies, and other priority investigations.
• Develops and maintains dashboards, recurring reports, loss reporting, benchmarking analyses, and fraud performance products.
• Performs data quality checks and validates the completeness, accuracy, and reasonableness of data used in reporting and detections.
• Documents analytical methodologies, assumptions, data lineage, testing results, and operating procedures in accordance with established standards.
• Translates analytical findings into clear, actionable insights for fraud operations, risk partners, technology teams, and other stakeholders.
• Collaborates with senior analysts and business partners to validate analytical approaches, resolve data issues, and deliver quality work products.
• Partners with investigative teams to obtain disposition feedback and identify opportunities to improve detection effectiveness.
• Contributes to cross-functional fraud initiatives, data modernization efforts, and evaluation of fraud tools or capabilities.
• Participates in special projects and performs other duties as assigned.

• Minimum of five years related work experience.
• Undergraduate degree or equivalent combination of training and experience.
• Intermediate SQL skills, including experience querying, joining, validating, and analyzing large datasets.
• Working proficiency in Python or another analytical programming language used for data preparation, automation, statistical analysis, or detection development.
• Experience developing and maintaining dashboards and reports in Tableau or a comparable visualization platform.
• Experience supporting the development, testing, monitoring, or optimization of fraud detections, risk rules, anomaly-detection methods, or analytical models.
• Working knowledge of analytical validation methods, data quality controls, and performance measurement.
• Ability to communicate technical findings clearly and provide actionable insights to technical and non-technical audiences.
• Ability to manage assigned work, maintain documentation, and deliver quality results within established timelines.
• Knowledge of financial-services fraud typologies, investigations, fraud operations, or fraud loss measurement preferred.
• Experience working in cloud-based data environments and with governed enterprise data assets preferred.