Head Of Data Science & Credit Risk

Accountabilities: ML and model development: Lead the design, testing, deployment, and ongoing improvement of machine learning models for credit decisioning, fraud detection, risk segmentation, customer value, monetization, and marketing attribution. Underwriting innovation: Develop underwriting algorithms using alternative data sources to strengthen risk assessment while responsibly expanding access to financial services. Real-time decisioning: Build and scale real-time or near-real-time scoring models across multiple markets and products. Model governance: Ensure models are interpretable, robust, fair, and reliable, with appropriate monitoring for accuracy, feature stability, performance, and drift. MLOps: Establish strong practices for experimentation, model versioning, deployment, monitoring, and production lifecycle management. Credit risk strategy: Develop and manage credit risk frameworks, policies, approval strategies, risk thresholds, and customer segmentation approaches adapted to individual markets. Portfolio monitoring: Track portfolio and risk metrics, investigate material changes, and establish early-warning indicators for potential deterioration. Experimentation: Simulate policy and model changes, lead A/B testing, and use performance data and business KPIs to continuously refine decisioning strategies. Stress testing and provisioning: Lead stress testing and expected credit loss modeling while partnering with Finance on provisioning and capital allocation. Market expansion: Develop localized risk models and policies that support expansion into new markets while aligning with applicable regulatory requirements. Team leadership: Build, lead, coach, and mentor data scientists and risk analysts while remaining hands-on with technical problem-solving and model development. Strategic planning: Own the data science and credit risk roadmap, aligning priorities with business growth, product development, and market expansion objectives. Executive communication: Present model performance, portfolio trends, analytical insights, and strategic recommendations clearly to executive leadership and board-level stakeholders. Cross-functional partnership: Work closely with Engineering, Product, and Finance to translate analytical findings into measurable business outcomes. External partnerships: Evaluate and establish relationships with alternative data providers and credit bureaus. Business impact: Improve approval rates while maintaining target default rates and responsible lending standards, reduce time-to-decision, strengthen unit economics, and identify new customer and product opportunities. Requirements: Professional experience: 10+ years of combined experience across data science, machine learning, and consumer credit risk, ideally within fintech, digital lending, BNPL, or earned wage access. Credit risk leadership: Proven experience developing and managing credit policies and portfolios at scale across multiple products, markets, or both. Production ML: Demonstrated success building, deploying, and monitoring production machine learning models within real-time or near-real-time decisioning environments. Experimentation: Strong hands-on experience with experimentation and A/B testing to assess the impact of model and policy changes. Statistical expertise: Strong mathematical and statistical foundations, combining classical statistical techniques with modern machine learning approaches. Data skills: Strong proficiency in SQL and exploratory data analysis, alongside practical experience working with cloud-based data platforms. Cloud technology: Experience with cloud data infrastructure is required; familiarity with GCP BigQuery is useful but experience with this specific platform is not mandatory. Technical leadership: Experience building and leading technical teams while remaining actively engaged in model development, analytical work, and complex problem-solving. Communication: Excellent communication skills, with the ability to explain sophisticated models, risk concepts, and analytical recommendations to non-technical stakeholders. Business judgment: Strong commercial understanding and the ability to connect technical and risk decisions with growth, portfolio performance, unit economics, and return on investment. Startup mindset: Adaptable, proactive, and comfortable operating in a fast-paced environment where priorities can evolve quickly. Regional expertise: Familiarity with Southeast Asian credit markets, credit bureaus, and alternative data sources is an advantage. MLOps tools: Experience with MLflow or similar frameworks for production machine learning development and deployment is a plus. Regulatory knowledge: Understanding of IFRS 9 and local credit regulations across multiple markets or regions is beneficial. Benefits: Competitive compensation: Salary based on experience and location. Equity participation: Opportunity to participate in the company’s equity program. Leadership opportunity: Build and shape a growing data science and credit risk function from the ground up. Professional growth: Opportunities to expand your leadership, technical expertise, and strategic influence within a rapidly growing organization. Modern technology: Work with a modern machine learning stack and cloud-based data infrastructure. International scope: Lead data science and credit risk initiatives across multiple markets and support international expansion. Meaningful impact: Help develop models and financial services designed to expand responsible access to financial products for underbanked employees. Mission-driven environment: Contribute to improving financial well-being through fairer, more accessible financial services.