Originally posted 22 September 2026 by the employer.

About the role

This role defines and guides the technical direction for scalable fraud technology and platforms.

What you'll do

Lead the technical strategy, architecture, and roadmap for fraud technology to support customers in building, customizing, and operating fraud detection, prevention, investigation, and decisioning systems.
Design reusable detection approaches combining rules, machine learning, anomaly detection, behavioral analytics, graph analytics, entity resolution, and risk scoring.
Partner with Data Science, ML Engineering, Product, and Customer Engineering teams to develop, evaluate, deploy, and continuously improve fraud detection capabilities for customer use cases.
Identify, prioritize, and integrate fraud signals across transactional, identity, account, device, network, application, behavioral, and operational data.
Establish frameworks for rapidly translating newly discovered fraud patterns into production rules, signals, models, and detection workflows.
Build reusable detection infrastructure, services, APIs, reference architectures, and frameworks that support multiple products, fraud types, customer environments, and business use cases.

Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related technical field, or equivalent experience.
15+ years of progressive experience in software engineering or a related technical discipline, including 6+ years of experience leading and managing complex, cross-functional engineering organizations and delivering high-impact technical products or platforms.
Deep expertise in one or more of the following areas: software engineering, fraud technology, risk systems, security engineering, machine learning, data science, or data engineering.
Significant experience designing, building, or operating large-scale fraud, abuse, risk, security, detection, or machine learning systems.
Experience developing platforms, products, APIs, services, or technical frameworks that are adopted by internal or external customers to build production systems.
Strong understanding of detection methodologies, including rules-based, statistical, behavioral, anomaly-based, graph-based, and machine-learning approaches.
Experience designing real-time, high-volume, distributed, or event-driven data processing systems.
Experience with data pipelines, feature engineering, model inference, production ML systems, or decisioning platforms.