Head Of Data Science & Credit Risk

Overview

Accountabilities

• Define and lead the end-to-end data science and credit risk strategy, including underwriting models, portfolio risk frameworks, and decisioning systems.
• Design and deploy advanced machine learning models for credit scoring, fraud detection, segmentation, and customer value optimization.
• Build real-time and near-real-time decisioning pipelines supporting scalable credit underwriting across multiple markets.
• Develop and continuously improve credit risk policies, approval strategies, and risk thresholds aligned with business growth and portfolio health.
• Establish MLOps standards for model deployment, monitoring, versioning, and performance tracking in production environments.
• Lead portfolio risk analytics, including stress testing, expected credit loss modeling, and early warning systems for deterioration.
• Partner with finance, product, and operations teams to optimize unit economics, provisioning strategies, and capital allocation.
• Translate complex analytical outputs into clear, actionable insights for executive leadership and board-level discussions.
• Drive experimentation culture, including A/B testing frameworks and data-driven product optimization.
• Build and scale partnerships with external data providers, credit bureaus, and alternative data ecosystems.
• Recruit, mentor, and develop a high-performing team of data scientists and risk analysts.

Requirements

• 10+ years of experience in data science, machine learning, and consumer credit risk within fintech, digital lending, BNPL, or EWA environments.
• Proven track record of building, deploying, and maintaining production ML models within real-time or near-real-time decisioning systems.
• Strong experience managing credit portfolios and designing credit policies across single or multi-market environments.
• Deep expertise in statistical modeling, machine learning techniques, and large-scale data analysis.
• Strong SQL skills and hands-on experience with cloud-based data platforms (e.g., GCP, BigQuery, or equivalent).
• Demonstrated leadership experience building and scaling technical teams while remaining technically engaged.
• Ability to communicate complex technical and analytical concepts clearly to business and executive stakeholders.
• Strong understanding of experimentation frameworks such as A/B testing and causal inference approaches.
• Experience in fintech risk, underwriting systems, or lending products with strong business impact.
• Familiarity with Southeast Asian credit markets, alternative data sources, or regulatory frameworks is a strong plus.
• Experience with MLOps tooling (e.g., MLflow or similar platforms) is highly desirable.

Benefits

• Competitive salary package aligned with experience and market standards
• Equity participation in a high-growth fintech scale-up
• Opportunity to build credit risk and ML capabilities from the ground up
• High-impact role directly contributing to financial inclusion across emerging markets
• Fast-paced, mission-driven environment with strong ownership and autonomy
• Access to modern ML stack and cloud-native infrastructure
• Strong career progression opportunities in a rapidly expanding organization
• Culture focused on experimentation, innovation, and data-driven decision-making

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