Lead, Credit Modeller & Data Scientist

Job Purpose

The Lead, Credit Modeller & Data Scientist oversees the development, validation, and optimization of credit scoring models, risk analytics frameworks, and data-driven decision systems. This role ensures that the organization adopts advanced statistical and machine learning techniques to enhance credit underwriting, customer segmentation, early warning systems, and portfolio performance.

Key Responsibilities

1. Credit Risk Modelling

 Lead the development of credit scoring models (application, behaviour, and collections

scorecards).

 Build PD, LGD, EAD models aligned with IFRS 9 and regulatory expectations.

 Develop and maintain automated risk decision engines for loan approvals.

 Conduct vintage analysis, survival modelling, and portfolio default forecasting.

2. Machine Learning & Advanced Analytics

 Design and deploy machine learning models such as random forests, gradient boosting,

neural networks, etc.

 Develop predictive models for fraud detection, customer churn, and collection

propensity.

 Implement AI/ML techniques to improve loan decision accuracy and reduce losses.

3. Model Validation & Monitoring

 Oversee model performance tracking using AUC, Gini, KS, precision/recall, ROC

curves, etc.

 Ensure periodic back-testing, recalibration, and revalidation of all credit models.

 Document model assumptions, limitations, validation processes, and change controls.

4. Data Infrastructure & Governance

 Ensure clean, consistent, and high-quality data for modelling purposes.

 Collaborate with IT/Data Engineering to build scalable datasets and modelling pipelines.

 Maintain data governance standards, metadata definitions, and feature stores.

5. Portfolio Analytics and Strategy

 Analyze credit portfolio trends including NPLs, roll rates, ECL drivers, and cohort

behaviour.

 Support credit policy reviews with data-driven insights and simulations.

 Contribute to capital adequacy, stress testing, and scenario analysis.

6. Decision Engine & Automation

 Integrate models into automated decision systems and underwriting workflows.

 Work with product, engineering, and credit teams to optimize risk-based pricing.

 Support real-time risk assessments for digital lending channels.

7. Leadership & Stakeholder Engagement

 Lead a team of analysts, modellers, and data scientists.

 Train and mentor staff in modelling and analytics.

 Present technical insights to senior management, Board committees, and regulators.

 Provide analytics to support new product development and risk strategy.

Qualifications & Experience

 Bachelor’s or Master’s degree in Statistics, Mathematics, Computer Science, Data

Science, Engineering, Finance, or related field.

 5–10+ years of experience in credit risk modelling or data science within a bank, fintech,

or microfinance institution.

 Strong knowledge of statistical modelling, machine learning, and credit risk frameworks.

 Proficiency in Python, R, SQL, SAS, or similar analytical tools.

 Experience with BI tools (Power BI, Tableau) and model deployment frameworks

(MLflow, AWS, Azure, etc.).
 Familiarity with Basel, IFRS 9, and local regulatory guidelines for credit risk.

 Previous leadership or team lead experience is an advantage.

Skills & Competencies

 Advanced modelling and machine learning expertise.

 Strong quantitative and analytical problem-solving skills.

 Ability to communicate complex analysis to non-technical stakeholders.

 Excellent documentation and data governance skills.

 Strategic thinking with the ability to deliver practical solutions.

 High ethical standards and commitment to responsible AI/ML practices.

Application Link:
https://forms.gle/nLcbAqRiGH1vEPrQ6

Job Type: Full-time

Experience:
• credit risk modelling within a bank, fintech, or MFB: 7 years (Required)
• Python, R, SQL, SAS, or similar analytical tools.: 7 years (Required)
• model deployment frameworks (MLflow, AWS, Azure, etc.).: 7 years (Required)
• Advanced modelling and machine learning expertise.: 7 years (Required)