Data Analyst, Specialist

Vanguard•Malvern, PA

1d•Hybrid

About The Position

Leads complex fraud analytics initiatives from problem definition and data assessment through implementation, measurement, and ongoing monitoring. Designs, develops, back-tests, and optimizes offline fraud detections that generate actionable leads for investigative teams. Establishes performance measures for detections, including precision, recall, false-positive rates, alert volumes, loss exposure, and prevented or avoided impact. Identifies emerging fraud trends, anomalous behavior, common attributes, and fraud signatures across account, client, transactional, and case data. Leads forensic analysis and analytical support for significant fraud incidents, control gaps, and emerging typologies. Develops and owns executive reporting, dashboards, benchmarking, loss reporting, and recurring fraud performance products. Defines data quality controls and validates the completeness, accuracy, and reasonableness of fraud data used in reporting and detection. Documents analytical methodologies, assumptions, data lineage, and control procedures to support governance and audit readiness. Translates complex analytical findings into concise recommendations for fraud operations, risk partners, technology teams, and senior leadership. Reviews analytical approaches and work products developed by other analysts and provides technical direction, coaching, and quality assurance. Partners with investigative teams to establish feedback loops and disposition processes that improve detection effectiveness. Serves as an analytical subject-matter expert on cross-functional fraud initiatives, data modernization efforts, and evaluation of fraud tools or capabilities.

Requirements

Minimum of five years of progressively responsible experience in data analytics, fraud analytics, risk analytics, or a related field.
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.
Undergraduate degree or equivalent combination of training and experience.

Nice To Haves

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.
Graduate degree preferred.

Responsibilities

Leads complex fraud analytics initiatives from problem definition and data assessment through implementation, measurement, and ongoing monitoring.
Designs, develops, back-tests, and optimizes offline fraud detections that generate actionable leads for investigative teams.
Establishes performance measures for detections, including precision, recall, false-positive rates, alert volumes, loss exposure, and prevented or avoided impact.
Identifies emerging fraud trends, anomalous behavior, common attributes, and fraud signatures across account, client, transactional, and case data.
Leads forensic analysis and analytical support for significant fraud incidents, control gaps, and emerging typologies.
Develops and owns executive reporting, dashboards, benchmarking, loss reporting, and recurring fraud performance products.
Defines data quality controls and validates the completeness, accuracy, and reasonableness of fraud data used in reporting and detection.
Documents analytical methodologies, assumptions, data lineage, and control procedures to support governance and audit readiness.
Translates complex analytical findings into concise recommendations for fraud operations, risk partners, technology teams, and senior leadership.
Reviews analytical approaches and work products developed by other analysts and provides technical direction, coaching, and quality assurance.
Partners with investigative teams to establish feedback loops and disposition processes that improve detection effectiveness.
Serves as an analytical subject-matter expert on cross-functional fraud initiatives, data modernization efforts, and evaluation of fraud tools or capabilities.