Fraud Analytics & Strategy Manager

Fraud Analytics & Strategy Manager

Candidates must work a hybrid schedule in one of these locations (3 days onsite weekly)

Dallas/Irving (TX), New Castle (DE), Jacksonville (FL), Sioux Falls (SD), New York City

As a key member of the Fraud Analytics, Modeling & Intelligence team, you will design, execute, and scale data-driven fraud strategies to protect consumer, small business, and commercial credit card portfolios as well as retail banking products.

In an evolving threat landscape, you will leverage advanced analytics to detect emerging trends, optimize decision engines, and mitigate attacks across the full fraud lifecycle-including synthetic identity fraud, account takeover (ATO), and complex new attack vectors. You will act as a critical bridge between data, strategy, policy, and model risk management to safeguard the firm and its customers.

• Strategy & Decision Engine Optimization: Design, test, and implement robust fraud mitigation rules and authorization governance strategies across decisioning systems, adhering to strict change-control standards.
• Model Risk Management & Lifecycle Oversight: Serve as a primary lead for Fraud Model Risk-partnering with modelers, external vendors, and internal validators across the model lifecycle (validation, ongoing performance monitoring, versioning, and annual reviews).
• Advanced Data Analytics & Trend Detection: Query large-scale datasets using SAS, SQL, or open-source tools to identify behavioral patterns, assess rule performance, and translate raw data into actionable insights for executive leadership.
• Regulatory Compliance & Technical Documentation: Package comprehensive analysis into technical documentation that meets regulatory guidelines and industry standards. Present model validation findings to senior management and supervisory authorities as required.
• Cross-Functional Leadership: Partner closely with Fraud Policy, Operations, Product, and IT teams to align fraud strategies with business growth, technology roadmaps, and incident management protocols.
• Continuous Innovation: Research and evaluate new data sources, analytical tools, and technology capabilities to continuously enhance our fraud detection architecture and process efficiencies.

• Education: Bachelor's degree in a quantitative discipline (Statistics, Mathematics, Economics, Data Science, or related field).
• Experience: 5+ years in fraud strategy, quantitative analytics, or risk management within financial services.
• Technical Mastery:
  
  
  
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    Intermediate to advanced proficiency in SAS or SQL for complex data manipulation, strategy monitoring, and performance assessments.

  • Strong hands-on proficiency in Excel for data modeling and reporting.
  • Experience working within Big Data environments (e.g., Python, Hive, Impala) and performing mathematical/statistical analysis on large datasets.
  
  
  
  
• Model Governance Experience: Demonstrated familiarity with model development, production deployment, and model risk governance frameworks.
• Communication & Influence: Proven ability to translate complex analytical findings into executive-ready presentations and interface confidently with regulatory auditors and senior leaders.

• Master's Degree in a quantitative or technical field.
• Domain Expertise: 2+ years of direct experience in the payments industry, particularly with credit card products, payment rails, Mastercard ecosystems, and associated fraud vectors.
• Project Management: Track record of driving cross-functional initiatives and managing system incident responses in a fast-paced environment.

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