Risk Engineering Architect - Payment (ML/DS background)

About Our Client

Our client is a leading payment company integrated with various integrations across businesses.

Job Description

• Collaborate cross-teams & departments to develop real-time risk decisioning systems.
• Create scalable frameworks that convert risk strategies into engineering solutions.
• Define best practices for monitoring, detecting, and mitigating fraud.
• Create machine learning models for fraud detection in payments.
• Use time-series methods to anticipate risk and fraud patterns.
• Partner with data scientists to enhance prediction accuracy.
• Build clear workflows for handling compromised accounts (e.g., ATO).
• Define resolution steps, exit criteria, and user recovery processes.
• Improve fraud response while maintaining a smooth customer experience.
• Investigate false positives/negatives in detection systems.
• Continuously refine rules and models using live feedback and develop adaptive risk scoring to stay ahead of emerging threats.

The Successful Applicant

• Strong backend engineering or real-time systems experience.
• Deep understanding of risk analysis, fraud detection, and anomaly detection and familiarity machine learning for fraud prevention and forecasting.
• Proven track record in building scalable, automated risk systems, with 10+ years in risk engineering, fraud analytics, or security engineering.
• Solid experience in risk, fraud, and payment security.
• Skilled in working across risk, engineering, and data teams.
• Strong analytical and problem-solving mindset.

What's on Offer

Opportunity to create cutting-edge technology solutions for risk management.
Grow risk analytics practices for diverse financial technology product portfolio.