Set the strategic direction for advanced analytics within EFCC, with an emphasis on AI, machine learning, and scalable cloud architectures.
Lead and develop a high-performing analytics team spanning emerging risk analytics, quantitative modeling, and data intelligence.
Translate regulatory expectations, emerging typologies, and business needs into analytically sound, defensible solutions.
Oversee development and enhancement of customer
and account-level risk models, anomaly detection, and typology-driven analytics.
Expand use of machine-learning frameworks and AI-enabled tooling (e.g., clustering, relationship intelligence, natural-language analysis) to surface risk not fully covered by traditional monitoring.
Guide iterative model improvement using new data sources, feedback loops from investigations, and performance metrics.
Remain hands-on as needed to unblock team members, prototype solutions, and credibly challenge analytical assumptions.
Assist in driving the transition from legacy statistical tooling toward open-source and cloud-ready solutions, improving flexibility and speed to insight.
Support development of open-source equivalents for existing analytics and reporting capabilities to enable future cloud adoption.
Coordinate rapid-response analytics for emerging financial crime risks, integrating subpoena data, human intelligence, transaction data, and external insights.
Lead analytics supporting new and evolving typologies (e.g., novel fraud schemes, sanctions evasion, trafficking-related activity).
Maintain strong feedback cycles with investigators and risk partners to refine coverage and prioritize effort.
Ensure clear, consistent documentation across analytics lifecycles, including model design, assumptions, validation artifacts, monitoring reports, and procedures.
Communicate complex analytical concepts clearly to executive leadership, regulators, auditors, and cross-functional partners.
Represent Strategic Analytics in regulatory exams, internal reviews, and enterprise-level discussions.