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
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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.
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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.
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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.
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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.
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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.
