Vanguard

Data Analyst, Fraud Data & Analytics

Vanguard Scottsdale, Arizona, United States

Financial Services · 10,001+ employees

Yesterday
data-analyst Senior (5-10 yrs) Full-time United States
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About the role

The Data Analyst leads complex fraud analytics initiatives, including designing and optimizing detection models and performing forensic analysis on fraud incidents. They also develop executive reporting, establish data quality controls, and provide technical guidance to cross-functional teams.

What they look for

Data analysis Fraud detection SQL Python Tableau Statistical analysis Risk management Data visualization Forensic analysis Anomaly detection Data quality control Performance measurement Technical documentation Stakeholder management Mentoring Financial services

Requirements

Candidates must have at least five years of related experience and an undergraduate degree. Proficiency in SQL, Python, and data visualization tools like Tableau is required, along with experience in fraud detection and risk rule optimization.

Benefits

Comprehensive health and wellness care Work-life balance Investment in your future

Full description

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.

Qualifications

  • 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

Global Risk and Security (GR&S) at Vanguard enables business strategy, protects client and Vanguard interests (e.g. assets and data), and stewards a strong risk culture. Our teams leverage enterprise-wide insights, deep expertise, and trusted advice so that across Vanguard leaders and crew drive faster, stronger, risk-informed decisions.

 

Within GR&S, the Enterprise Security and Fraud (ES&F) sub-division is responsible for the global protection of Vanguard crew, property, data, and client assets. We are the trusted advisors that protect the pride of Vanguard with state-of-the-art security and fraud capabilities. We are a world-class destination of highly engaged, passionate, and diverse talent expected to continuously learn and develop in an ever-changing security landscape. Our crew are our greatest resource – by joining our team you will build collaborative long-term relationships and enjoy a suite of benefits that includes comprehensive health and wellness care, work-life balance, and an investment in your future at its core.

Special Factors

Sponsorship

Vanguard is not offering visa sponsorship for this position.

About Vanguard

At Vanguard, we don't just have a mission—we're on a mission.

To work for the long-term financial wellbeing of our clients. To lead through product and services that transform our clients' lives. To learn and develop our skills as individuals and as a team. From Malvern to Melbourne, our mission drives us forward and inspires us to be our best.

How We Work

Vanguard has implemented a hybrid working model for the majority of our crew members, designed to capture the benefits of enhanced flexibility while enabling in-person learning, collaboration, and connection. We believe our mission-driven and highly collaborative culture is a critical enabler to support long-term client outcomes and enrich the employee experience.

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