Head of Risk Architect (Fintech)

Keyword: Risk, Architect, Data Science, Engineering

We are looking for a Head of Risk Architect for a leading fintech company. The primary objective of this role is to deploy risk strategies into credit modeling systems, ensuring robust fraud prevention, real-time decisioning, and scalable risk frameworks.

What You’ll Do

1. Risk-Engineering Integration

   • Partner with Risk Analytics and Engineering teams to design and implement real-time risk decision systems.
   • Build scalable frameworks that translate risk principles into actionable engineering solutions.
   • Define and enforce best practices for risk monitoring, fraud detection, and mitigation.

2. Incident Response & Recovery Frameworks

   • Establish clear workflows for compromised accounts (e.g., ATO scenarios).
   • Develop resolution processes, exit criteria, and user recovery mechanisms.
   • Enhance customer experience during fraud resolution while maintaining security standards.

3. Risk Analytics & Model Improvement

   • Analyze false positives and false negatives to improve detection accuracy.
   • Continuously refine risk rules and fraud models based on real-world feedback.
   • Implement dynamic risk scoring models that adapt to emerging threats.

4. Automation & End-to-End Risk Flow

   • Automate risk assessment, fraud prevention, and incident response workflows.
   • Integrate machine learning-driven decision-making into fraud detection systems.
   • Develop real-time monitoring and adaptive risk strategies.

5. Advanced Risk Modeling & Forecasting

   • Design ML-based fraud detection models for payments and transactions.
   • Apply time-series forecasting to anticipate payment risks and fraudulent behaviors.
   • Collaborate with data science teams to improve predictive accuracy.

What You'll Bring

• Engineering & Development Expertise: Strong experience in backend engineering, real-time decision systems, or security automation.
• Risk & Fraud Analytics Knowledge: Deep understanding of risk modeling, fraud detection, and anomaly detection techniques.
• Machine Learning & Data Science Familiarity: Exposure to ML models for fraud prevention, time-series forecasting, and automated risk detection.
• Automation & Scalability Skills: Proven ability to build automated pipelines for risk mitigation and decisioning.
• Experience & Background:
  • 10+ years in risk-focused engineering, fraud analytics, or security engineering roles.
  • Solid background in risk management, payment security, or fraud prevention.
  • Experience in financial services, fintech, e-commerce, or digital payments.
  • Ability to work cross-functionally with risk, engineering, and data teams.
  • Strong problem-solving skills with a data-driven mindset.

Due to the high volume of applications we are experiencing, our team will only be in touch with you if your application is shortlisted.