Group Head of Scorecard Development

Overall Purpose of the Role:

The Head of Scorecard Development is responsible for leading the design, development, calibration, and governance of credit risk models and decisioning frameworks across products and markets.

This role sits within the centralized Credit Analytics Centre of Excellence and is accountable for building best-in-class models that enable Lending Heads (in-country P&L owners) to define and execute optimal credit strategies.

The role combines deep technical expertise with strong commercial understanding, ensuring that models are not only statistically robust, but also effectively translated into business decisions that drive portfolio profitability and resilience.

Experience and Skills Required:

• 10+ years in credit risk modelling, scorecards, or decision analytics.
• Proven experience building and deploying models in automated lending environments.

Deep expertise in:

• Statistical modelling and machine learning
• Feature engineering
• Model calibration and validation

Strong understanding of:

• Credit lifecycle dynamics
• NPV and unit economics frameworks
• Model performance metrics (Gini, KS, ROC-AUC) and their limitations

Experience working closely with:

• Product, engineering, and data teams
• Business / commercial stakeholders

• Strong programming capability (Python/R, SQL).
• High level of AI proficiency.
• Experience in multi-market or emerging market environments advantageous.

Personal attributes

• Combines deep technical expertise with commercial awareness.
• Thinks beyond models to business outcomes.
• Is comfortable challenging and advising senior stakeholders.
• Operates with strong ownership and accountability.
• Thrives in high-growth, ambiguous environments.

Reporting line and location

• Flexible / multi-market collaboration
• South African based role, reporting into the Group structure.

Key Responsibilities

Credit Modelling Strategy & Leadership:

• Define and lead the credit modelling strategy across products and markets.
• Set standards for model development, validation, calibration, and monitoring.
• Build and lead a high-performing team of scorecard developers and analysts.
• Act as the central point of expertise for credit modelling across the organisation.

Model Development & Decision Science:

• Oversee development of:
• Application and behavioural scorecards
• Affordability and fraud models
• Segmentation and risk frameworks
• Ensure models balance:
• Predictive power
• Stability and robustness
• Explainability and fairness
• Regulatory compliance
• Guide the use of advanced analytics and machine learning within governance constraints.

Calibration & Commercial Translation:

• Translate model outputs into actionable business metrics and decision frameworks.
• Define calibration methodologies linking scores to:
• Probability of default
• Loss expectations
• Pricing and limit strategies
• Partner with Lending Heads to:
• Optimize approval strategies
• Inform pricing and offer design
• Balance risk vs growth trade-offs
• Ensure models support NPV, unit economics, and embedded value optimisation.

Model Performance, Monitoring & Limitations:

• Define and oversee frameworks for model performance monitoring and reporting.
• Drive ongoing model recalibration and refresh cycles.
• Identify and communicate:
• Model limitations
• Bias and fairness risks
• Overfitting and calibration issues
• Ensure model performance is evaluated in the context of portfolio outcomes, not just statistical metrics.

Foundational Testing & Insight Generation:

• Partner with Lending Heads to design and support Foundational Tests.

• Identify model blind spots and areas of uncertainty.
• Use experimentation to generate high-quality data and improve model performance.
• Incorporate learnings into model evolution and strategy refinement.

Governance & Model Risk Management:

• Establish and maintain model governance frameworks.
• Ensure full documentation, audit readiness, and regulatory compliance.
• Support 2nd Line Risk in validation, review, and governance processes.
• Maintain transparency and explainability of models.

Data, Platform & Decisioning Infrastructure:

• Partner with Engineering and Data teams to productionise models.

• Ensure scalability, reproducibility, and version control.
• Define requirements for real-time decisioning systems.
• Support development of robust decision engines.