Head of Fraud & Risk (Payment Orchestration)

About the role

We are seeking a Head of Fraud & Risk to build and lead a next‑generation, vendor‑agnostic risk function for a leading payment orchestration platform. The role will shape a fraud and risk capability that serves both merchant‑facing products and internal intelligence, covering traditional payments, crypto and stablecoin flows.

Key responsibilities

• Design and implement a vendor‑agnostic risk architecture that integrates and swaps fraud, compliance and blockchain intelligence providers.
• Build and scale a hybrid risk engine combining rules and machine‑learning models for adaptive fraud detection and dynamic scoring.
• Create merchant‑facing risk tools such as dashboards, alerting systems and configurable rule engines.
• Embed risk signals into orchestration workflows to optimise routing across multiple PSPs.
• Integrate on‑chain analytics for crypto payments, including wallet screening, transaction tracing and tainted‑fund detection.
• Monitor fraud, decline and chargeback trends, supporting partners to meet scheme thresholds.
• Collaborate with finance and treasury to model liquidity and settlement risks for hybrid IBAN and stablecoin flows.
• Define and enforce a Risk Management Framework and Fraud Control Strategy aligned with PCI DSS.
• Lead vendor selection, integration and performance evaluation for fraud, compliance and blockchain intelligence solutions.

Required profile

• 7+ years of experience in fraud and risk within payment orchestration, PSPs or fintech.
• Deep understanding of payment ecosystems, PSPs, acquirers, alternative payment methods and transaction flows.
• Proven track record designing and managing fraud detection systems, including rules engines and machine‑learning models.
• Strong knowledge of PCI DSS requirements and payment security best practices.
• Experience working cross‑functionally with product, engineering and business teams.

Required skills

• Machine Learning
• Rules Engine development
• PCI DSS compliance
• API integration
• Fraud detection system design
• Risk modeling and analytics
• Data analysis