Head Of Credit Risk & Data Science

Position: Head of Credit Risk & Data Science

Department: Credit Risk & Data Science

Location: Global

FINN is a fintech company building simple and transparent products that improve financial well-being for underbanked employees. Our core offering gives people access to wages they have already earned, alongside tools that help them manage money better, build savings, access insurance, earn additional income, and use fairer financial services.

Founded in 2022, FINN is the largest player in the early wage access space in Southeast Asia and is expanding rapidly into new markets globally. We are looking for ambitious builders who take ownership, move fast, and want to shape products that create meaningful impact in people's lives.

We are looking for a Head of Credit Risk to own and lead FINN's credit risk strategy, underwriting policies, and portfolio performance as we expand across Southeast Asia and other markets.

This is a senior risk leadership role focused on balancing sustainable growth, responsible lending, and portfolio profitability. You will own the end-to-end credit risk lifecycle, from risk appetite and underwriting strategy to portfolio monitoring, credit policy optimization, and loss management.

You will leverage data science and machine learning to strengthen credit decisioning, improve risk segmentation, and expand access to financial services for underserved customers. You will lead a team of risk analysts and data scientists, ensuring that models and analytical tools translate into effective credit policies and measurable business outcomes.

Working closely with Product, Engineering, Finance, and executive leadership, you will establish a scalable, data-driven credit risk function that supports FINN's growth while maintaining strong portfolio quality.

• Own FINN's credit risk strategy, risk appetite, underwriting policies, and approval frameworks across products and markets.
• Develop and continuously optimize credit policies, risk thresholds, and customer segmentation strategies to balance approval rates, credit losses, and risk-adjusted returns.
• Own portfolio performance monitoring, including delinquency, roll rates, default rates, loss rates, and other key credit risk indicators.
• Establish early warning systems to identify emerging portfolio risks and implement timely corrective actions.
• Lead portfolio reviews, risk assessments, scenario analysis, and stress testing to evaluate portfolio resilience under different economic and business conditions.
• Partner with Finance on expected credit loss modeling, provisioning, and portfolio-level financial risk assessments.
• Ensure credit risk policies are appropriate for each market and aligned with applicable regulatory requirements and responsible lending standards.
• Develop and maintain clear risk governance frameworks, escalation processes, and reporting for executive leadership.

• Own the end-to-end underwriting strategy, including customer eligibility, credit assessment, approval rules, and risk-based decisioning.
• Improve approval rates and customer conversion while maintaining target default rates and portfolio quality.
• Develop and refine risk segmentation strategies to identify underserved customer segments with attractive risk-adjusted returns.
• Use portfolio performance data and customer behavior to continuously improve underwriting policies and credit decisions.
• Design and evaluate controlled experiments, policy tests, and champion-challenger strategies to measure the impact of underwriting changes.
• Partner with Product and Engineering to implement scalable, automated credit decisioning processes that reduce time-to-decision and friction in the customer journey.
• Evaluate alternative data sources, credit bureaus, and external partnerships to strengthen risk assessment and improve coverage of underserved populations.

• Lead the application of data science and machine learning to credit scoring, underwriting, risk segmentation, and fraud detection.
• Define model requirements and work with data scientists and engineers to develop, validate, deploy, and continuously improve credit risk models.
• Ensure models translate into effective underwriting policies and measurable improvements in approval rates, losses, and risk-adjusted portfolio performance.
• Establish appropriate model monitoring, validation, interpretability, fairness, and drift detection practices.
• Oversee model experimentation, performance evaluation, and ongoing optimization across products and markets.
• Ensure credit decisioning systems are scalable, reliable, and suitable for real-time or near-real-time decisions.
• Provide technical direction to the data science team while maintaining sufficient hands-on understanding to challenge assumptions, assess model quality, and guide technical decisions.

• Build, lead, and develop a high-performing team of credit risk professionals, analysts, and data scientists.
• Establish clear ownership, priorities, performance expectations, and development plans across the risk and data science functions.
• Own the credit risk roadmap and align priorities with business objectives, product development, and market expansion.
• Partner with Product, Engineering, Finance, and Operations to embed risk considerations into business decisions and customer experiences.
• Present portfolio performance, emerging risks, risk strategy, and recommendations to executive leadership and the board.
• Build a culture of accountability, experimentation, responsible risk-taking, and evidence-based decision-making.

Success in this role will be measured primarily by the strength and sustainability of FINN's credit portfolio and the effectiveness of its underwriting strategy.

Key outcomes include:

• Portfolio quality: Maintain delinquency, default, and credit loss rates within agreed risk appetite.
• Risk-adjusted growth: Increase approvals and customer access while preserving sustainable portfolio returns.
• Underwriting effectiveness: Improve credit decision accuracy, risk segmentation, and policy performance.
• Operational efficiency: Reduce time-to-decision and manual intervention through effective automated underwriting.
• Early risk detection: Identify portfolio deterioration promptly and implement effective mitigation strategies.
• Model effectiveness: Demonstrate measurable improvements in credit outcomes through ML-driven decisioning and model optimization.
• Scalable risk management: Establish consistent risk frameworks that can adapt to new products, markets, and regulatory environments.
• Team effectiveness: Build a strong credit risk and data science function with clear accountability and technical capability.

• At least 10 years of relevant experience across credit risk, consumer lending, credit analytics, or data science, with substantial experience leading credit risk strategy and managing consumer credit portfolios.
• Proven ownership of credit policies, underwriting frameworks, risk appetite, and portfolio performance within fintech, digital lending, consumer finance, BNPL, digital banking, or a related financial services business.
• Demonstrated success balancing approval growth, credit losses, portfolio quality, and risk-adjusted returns.
• Experience managing credit risk across multiple products, customer segments, or markets.
• Strong understanding of credit risk metrics, portfolio monitoring, delinquency trends, default behavior, loss forecasting, and risk mitigation.
• Experience applying credit scoring, statistical modeling, or machine learning to underwriting and credit decisioning.
• Sufficient technical depth to evaluate ML-based credit models, challenge model assumptions, and guide data scientists in model development and deployment.
• Experience leading and developing teams of credit risk professionals, analysts, and/or data scientists.
• Strong analytical and quantitative skills, including the ability to interpret model outputs and translate insights into actionable credit policies.
• Strong commercial judgment and the ability to connect credit decisions to customer acquisition, portfolio profitability, and business growth.
• Excellent communication skills, with the ability to influence senior stakeholders and explain risk trade-offs clearly.
• A proactive, adaptable approach suited to a fast-growing, international startup environment.

• Experience in Southeast Asian credit markets, including familiarity with local credit bureaus, alternative data sources, and lending practices.
• Experience in earned wage access, consumer fintech, or lending products serving underbanked customers.
• Familiarity with ML model deployment, MLOps, model monitoring frameworks, and experimentation platforms.
• Understanding of IFRS 9, expected credit loss modeling, and applicable credit regulations across multiple markets.
• Experience launching credit products or establishing risk frameworks in new markets.

• Competitive compensation based on experience and location.
• Equity participation.
• The opportunity to build and shape FINN's credit risk function as the business scales.
• The opportunity to influence credit strategy, underwriting, and portfolio performance across multiple markets.
• The opportunity to work with modern data science and machine learning capabilities.
• A mission-driven environment focused on improving financial well-being and expanding responsible access to financial services.

• Meaningful Impact: Help expand responsible financial access for underbanked employees.
• Strategic Ownership: Shape credit risk strategy and portfolio performance at a growing fintech.
• Greenfield Opportunity: Build scalable underwriting and risk management capabilities from the ground up.
• Technology-Enabled Risk Management: Apply data science and machine learning to improve credit decisions and business outcomes.
• Global Growth: Help establish sustainable credit risk frameworks as FINN enters new markets.
• Mission-Driven Work: Contribute to fairer, more transparent financial services.

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