Head of Data Science & Credit Risk

Role overview

This opportunity is on behalf of a partner employer that will handle applications and all subsequent hiring steps. The role is for a Head of Data Science & Credit Risk located in Ireland.

You will take ownership of the company’s credit intelligence agenda at a senior leadership level, sitting where machine learning, lending risk, and financial inclusion intersect. The mandate is to design, scale, and continually improve the decision systems that support lending across several Southeast Asian markets. You will guide end-to-end model strategy, from development through production deployment, while keeping portfolio health, profitability, and growth in balance. This is a hands-on leadership position in a fast-moving, mission-focused environment, offering the chance to build credit risk and ML capabilities from the ground up in a rapidly growing fintech business.

What you will do

• Own the full strategy for data science and credit risk, covering underwriting models, portfolio risk structures, and automated decisioning engines.
• Create and roll out machine learning models for credit scoring, fraud detection, customer segmentation, and customer value improvement.
• Develop decision pipelines that operate in real time or near real time to support scalable underwriting across multiple markets.
• Refine credit policies, approval logic, and risk limits so they support both business expansion and portfolio quality.
• Set MLOps practices for production deployment, monitoring, version control, and ongoing model performance management.
• Run portfolio analytics such as stress testing, expected credit loss estimation, and early-warning monitoring for portfolio deterioration.
• Work closely with finance, product, and operations to improve unit economics, provisioning approaches, and capital deployment.
• Turn complex analytics into practical recommendations for senior executives and board-level conversations.
• Promote a strong experimentation mindset, including A/B testing and product optimization driven by data.
• Expand and manage relationships with external data vendors, credit bureaus, and alternative data partners.
• Hire, coach, and grow a high-performing team of data scientists and risk analysts.

What the role calls for

• At least 10 years of experience across data science, machine learning, and consumer credit risk in fintech, digital lending, BNPL, or EWA settings.
• A strong history of developing, launching, and supporting production ML models in real-time or near-real-time decision systems.
• Substantial experience managing credit portfolios and shaping credit policies in one or more markets.
• Advanced capability in statistical modeling, machine learning methods, and large-scale data analysis.
• Strong SQL ability and practical exposure to cloud data platforms such as GCP, BigQuery, or similar tools.
• Prior leadership experience building and scaling technical teams while staying technically involved.
• Clear communication skills with the ability to explain technical and analytical topics to business and executive audiences.
• Working knowledge of experimentation methods such as A/B testing and causal inference.
• Relevant background in fintech risk, underwriting systems, or lending products with strong commercial impact.
• Exposure to Southeast Asian credit markets, alternative data sources, or regulatory environments would be an advantage.
• Hands-on familiarity with MLOps tools such as MLflow or comparable platforms is highly desirable.

Benefits and highlights

• Competitive compensation aligned with experience and market benchmarks.
• Equity participation in a fast-growing fintech scale-up.
• The chance to create credit risk and machine learning capabilities from scratch.
• A high-impact position supporting financial inclusion in emerging markets.
• A fast-paced, mission-led culture with significant ownership and autonomy.
• Access to a modern machine learning stack and cloud-native infrastructure.
• Strong opportunities for career growth in an expanding organization.
• A culture that values experimentation, innovation, and decisions grounded in data.

Additional information

This role is managed through a partner hiring process. Applications are reviewed through an AI-assisted matching workflow that helps prioritize candidates based on fit with the role’s core requirements. A shortlist is then shared with the hiring company, while interviews, assessments, and final decisions are handled internally by their team.

Privacy and data processing notice

By submitting an application, candidates acknowledge that personal data may be processed to assess candidacy and share relevant details with the hiring employer, in line with applicable data protection laws, including GDPR. Candidates may exercise rights relating to access, correction, deletion, and objection at any time. AI tools may assist with tasks such as application review, résumé analysis, and response evaluation, but final hiring decisions remain human-led.