Own Antifraud & Disputes Analytics end-to-end — metrics, dashboards, monitoring, data quality, and analytical support for business decisions
Define the team's roadmap and priorities, allocating resources to the highest-impact problems
Build and own ML models for Antifraud & Disputes across the full lifecycle — from feature engineering to deployment and monitoring
Manage antifraud-related costs (vendors, tooling, operations) and drive efficiency without compromising risk or customer experience
Partner with Product and Engineering to shape solutions and influence priorities for fraud prevention and disputes
Optimise Fraud Operations at scale, working with a 150+ person team through data, automation, and process redesign
Build self-service analytics and LLM-based tools so other teams can answer common questions independently
Define and track the framework for measuring antifraud impact — losses, approval/conversion rates, disputes, costs, and friction
Turn complex fraud and dispute problems into structured investigations and solutions across rules, ML, product, and operations
Evaluate and manage external vendors — testing, benchmarking, and driving adoption decisions