Data Analyst, Fraud Data & Analytics

• Lead complex fraud analytics initiatives from problem definition and data assessment through implementation, measurement, and ongoing monitoring
• Design, develop, back-test, and optimize offline fraud detections that generate actionable leads for investigative teams
• Establish performance measures for detections, including precision, recall, false-positive rates, alert volumes, loss exposure, and prevented or avoided impact
• Identify emerging fraud trends, anomalous behavior, common attributes, and fraud signatures across account, client, transactional, and case data
• Lead forensic analysis and analytical support for significant fraud incidents, control gaps, and emerging typologies
• Develop and own executive reporting, dashboards, benchmarking, loss reporting, and recurring fraud performance products
• Define data quality controls and validate the completeness, accuracy, and reasonableness of fraud data used in reporting and detection
• Document analytical methodologies, assumptions, data lineage, and control procedures to support governance and audit readiness
• Translate complex analytical findings into concise recommendations for fraud operations, risk partners, technology teams, and senior leadership
• Review analytical approaches and work products developed by other analysts and provide technical direction, coaching, and quality assurance
• Partner with investigative teams to establish feedback loops and disposition processes that improve detection effectiveness
• Serve as an analytical subject-matter expert on cross-functional fraud initiatives, data modernization efforts, and evaluation of fraud tools or capabilities

• Minimum of five years related work experience
• Undergraduate degree or equivalent combination of training and experience
• Demonstrated experience independently leading complex analytical initiatives and influencing business decisions
• Advanced SQL skills, including experience analyzing large and complex datasets
• Proficiency in Python or another analytical programming language used for data preparation, automation, statistical analysis, or detection development
• Advanced experience developing dashboards and executive reporting in Tableau or a comparable visualization platform
• Experience developing, testing, monitoring, or optimizing fraud detections, risk rules, anomaly-detection methods, or analytical models
• Strong understanding of analytical validation methods, data quality controls, and performance measurement
• Ability to translate technical findings into concise, actionable recommendations for technical and non-technical audiences
• Demonstrated ability to review peer work, establish analytical standards, and coach less-experienced analysts
• Knowledge of financial-services fraud typologies, investigations, fraud operations, or fraud loss measurement strongly preferred
• Experience working in cloud-based data environments and with governed enterprise data assets preferred
• Vanguard is not offering visa sponsorship for this position

Demonstrates expertise in leading complex fraud analytics initiatives, including the design and optimization of fraud detection methods, performance measurement, and executive reporting. Proficient in translating analytical findings into actionable recommendations for diverse audiences while ensuring data quality and governance.

• Advanced SQL Skills
• Python Programming
• Dashboard Development in Tableau
• Fraud Detection Optimization
• Analytical Validation Methods

• Fraud Analytics
• Data Assessment
• Performance Measurement
• Anomaly Detection
• Statistical Analysis
• Data Quality Controls
• Analytical Methodologies
• Fraud Loss Measurement
• Risk Rules Development
• Executive Reporting

• Influencing Business Decisions
• Coaching Analysts
• Translating Technical Findings
• Collaboration with Investigative Teams
• Quality Assurance

• Financial-Services Fraud Typologies
• Fraud Operations
• Fraud Investigations
• Fraud Performance Products
• Data Modernization Efforts

• Tableau
• Cloud-Based Data Environments
• Governed Enterprise Data Assets