Head of Fraud & Risk Data Science

About the Role

Lead a high-impact team transforming raw risk and payments data into real-time decisions that power a leading fraud prevention platform. This hands-on leadership role is critical for driving customer success and significantly reducing fraud for enterprise clients.

Responsibilities

• Lead end-to-end Proof-of-Concept (POC) and Proof-of-Value (POV) projects with prospective customers, demonstrating fraud loss reduction and ROI.
• Design, optimize, and scale real-time decision systems by integrating machine learning models, features, and rules across billions of events.
• Recruit, coach, and mentor a high-performing team of fraud analysts and data scientists.
• Implement robust experimentation frameworks (e.g., A/B tests, bandit models) to safely validate new strategies.
• Collaborate with Product, Engineering, and Commercial teams to embed data-driven insights into dashboards, APIs, and pricing strategies.
• Present complex findings and strategic recommendations clearly and compellingly to customers, executives, and external stakeholders.
• Drive roadmap development for new signals, feature builds, and feedback loops to continuously enhance platform intelligence and speed.

Requirements

• 7+ years of experience in fraud analytics, data science, or machine learning, with 2+ years leading technical teams shipping real-time risk solutions.
• Proven track record of significantly reducing fraud losses and false positives for consumer and/or B2B products handling millions of daily transactions.
• Strong background in applied machine learning, anomaly detection, and experimentation design.
• Advanced SQL proficiency and familiarity with modern data infrastructure (e.g., Kafka, Flink, feature stores, vector databases).
• Demonstrated executive presence and storytelling ability, effectively communicating complex data insights to both technical and non-technical audiences.
• Experience owning and delivering POC/POV workstreams with demonstrable customer outcomes.