Head of Data Science & Credit Risk

Role overview

This is a senior leadership opportunity with a partner employer in Saudi Arabia. The role sits at the junction of machine learning, credit risk, and financial access, with responsibility for building and scaling the decisioning engine that supports lending across several Southeast Asian markets.

You will shape the full credit intelligence agenda, covering model creation, deployment, portfolio oversight, and business impact. The position calls for a leader who can guide a cross-functional team of data scientists and risk analysts, strengthen underwriting capabilities, and help steer credit policy, risk appetite, and market expansion. The setting is fast-moving, highly analytical, and focused on measurable outcomes, experimentation, and scalable systems.

Accountabilities

• Set and drive the complete strategy for data science and credit risk, including underwriting models, portfolio risk architecture, and decisioning logic.
• Create and launch advanced machine learning solutions for credit scoring, fraud prevention, customer segmentation, and value optimization.
• Develop real-time and near-real-time decisioning workflows that can support lending at scale across multiple markets.
• Refine credit policies, approval rules, and risk cutoffs so they support both growth objectives and portfolio quality.
• Put in place MLOps practices for deployment, monitoring, model version control, and production performance tracking.
• Lead portfolio analytics work such as stress testing, expected credit loss estimation, and early warning indicators for deterioration.
• Work closely with finance, product, and operations teams to improve unit economics, provisioning plans, and capital allocation decisions.
• Present complex analytical findings in a clear, actionable way for executive and board-level audiences.
• Promote a culture of experimentation through A/B testing and evidence-based product improvement.
• Expand and manage relationships with external data vendors, credit bureaus, and alternative data partners.
• Hire, coach, and develop a strong team of data scientists and risk analysts.

Requirements

• At least 10 years of experience in data science, machine learning, and consumer credit risk, ideally within fintech, digital lending, BNPL, or EWA settings.
• Hands-on experience building, deploying, and maintaining production ML models in real-time or near-real-time decision systems.
• Proven ability to manage credit portfolios and design credit policies for one or multiple markets.
• Strong command of statistical modeling, machine learning methods, and large-scale data analysis.
• Advanced SQL skills and practical experience with cloud data platforms such as GCP, BigQuery, or similar tools.
• Demonstrated people leadership experience, with the ability to grow technical teams while staying technically involved.
• Ability to explain sophisticated technical and analytical concepts to business and executive stakeholders in a clear manner.
• Solid understanding of experimentation methods, including A/B testing and causal inference.
• Relevant background in fintech risk, underwriting systems, or lending products with clear business impact.
• Knowledge of Southeast Asian credit markets, alternative data, or regulatory environments would be an advantage.
• Experience with MLOps platforms such as MLflow or comparable tools is preferred.

Benefits

• Competitive compensation package aligned with experience and market benchmarks.
• Equity participation in a fast-growing fintech scale-up.
• The chance to build credit risk and machine learning capabilities from the ground up.
• A meaningful role that contributes to financial inclusion in emerging markets.
• A high-ownership, mission-led environment with strong autonomy.
• Access to a modern machine learning stack and cloud-native infrastructure.
• Clear opportunities for progression in a rapidly expanding organization.
• A culture that values experimentation, innovation, and data-led decisions.

Additional information

This job is listed on behalf of a partner company, and the partner employer is responsible for reviewing applications and handling next steps.

Data privacy and hiring process

Applications are screened through an AI-assisted matching process to compare candidates against the role’s core requirements. The strongest matches are shortlisted and shared with the hiring company, while interviews, assessments, and final decisions are managed internally by the employer.

By applying, you agree that your personal data may be processed to assess your candidacy and shared with the hiring employer under applicable data protection rules, including GDPR where relevant. You can request access, correction, deletion, or objection to processing at any time.

AI tools may also be used to support parts of recruitment, such as reviewing applications, analyzing CVs, or checking for inconsistencies in application materials. These tools assist recruiters but do not replace human judgment, and final hiring decisions are made by people.