Senior Manager, Fraud Analytics, Operations

Responsibilities

• Set Fraud Strategy & Coverage:
  Define fraud prevention strategy across real-time payment channels, including risk appetite and control coverage.
• Own Real-Time Decisioning:
  Design and manage fraud rules, decision logic, and detection strategies within real-time decisioning platforms.
• Lead Platform & Capability Build:
  Drive enhancements and implementation of fraud rules engines and decisioning platforms in partnership with engineering.
• Optimize Performance Outcomes:
  Monitor and improve fraud detection, false positives, and client friction through ongoing control tuning and performance management.
• Leverage Data & Models:
  Use analytics and model insights to identify trends, refine strategies, and improve detection effectiveness.
• Partner Cross-Functionally:
  Collaborate with Product, Engineering, Risk, and Operations to embed fraud controls into customer journeys and new product launches.
• Influence Senior Stakeholders:
  Communicate fraud risk, performance, and trade-offs clearly through strong executive storytelling.
• Lead & Develop Team:
  Build and manage a high-performing team of fraud strategy and analytics professionals.
• Ensure Strong Governance:
  Maintain disciplined control testing, monitoring, and adherence to risk and regulatory standards.

• 10+ years in fraud risk, payments risk, or financial crimes
• Deep expertise in:
  Account takeover, social engineering, and transaction fraud risks within retirement and workplace channels
• 6+ years of people management experience leading high-performing teams
• Proven experience managing real-time fraud decisioning environments and rules strategies
• Strong experience partnering across Product, Engineering, Risk, and Operations
• Bachelor’s degree in Statistics, Mathematics, Data Science, Finance, Computer Science, or a related field.
• Graduate degree preferred.
• Expertise in fraud rules engines / decisioning platforms
• Strong analytical capabilities (data analysis, segmentation, performance optimization)
• Working knowledge of fraud models / machine learning concepts and model governance
• Ability to translate data into actionable fraud strategies and business decisions
• Strong experience with analytical tools such as Python, R, SAS, SQL, and platforms such as Tableau or Power BI.