Expert Data Scientists on Credit Scoring at Tezza Business Solutions Ltd

Job Purpose Statement:

• Data Science is a function within Data Management Office whose role is to transform raw data into actionable insights that drive smarter decision-making, stronger risk management and improved customer experiences. Leveraging on advanced analytics, statistic modelling and machine learning, the function supports in the areas such as Credit scoring, Fraud detection,
• Customer segmentation, Product personalization, risk forecasting and regulatory compliance. Seeking an experienced Data Scientist with proven expertise in credit scoring in financial institutions. The candidate will take ownership of building robust credit scorecards, ensuring delivery of a production-ready, regulatory-compliant solution that drives lending decisions i.e. Risk based pricing.
• Data Science and Innovation

Responsibility:

• Lead and implement the end-to-end development of credit scorecards, from problem definition to deployment
• Conduct data exploration, cleaning, transformation, and feature engineering.
• Apply statistical modeling and machine learning techniques (i.e, logistic regression, decision trees, ensemble methods, neural networks) for scorecard development.
• Perform model validation, back-testing, and monitoring to ensure stability, accuracy, and fairness.
• Collaborate with risk, credit, and technology teams to integrate models into production systems.
• Document methodology, assumptions, and outcomes for monitoring/governance.
• Train and mentor internal staff on scorecard use, monitoring, and governance
• Output:
• Fully developed credit scorecard models for retail, SME, or corporate lending (as applicable).
• Detailed model documentation (methodology, validation results, monitoring framework).
• Deployment-ready model integrated into production environment.
• Model performance tracking framework (e.g., KS, Gini, ROC AUC, stability index, population stability).
• Knowledge transfer to internal data science/risk teams

Job Dimensions

• Stakeholder Management: key stakeholders that the position holder will need to liaise/work with to be successful in this role.
• Internal: Data Engineers, IT, Credit, Operations, Customer Experience, Business Performance function, Business Support Services

Ideal Job Specifications

Academic:

• Bachelor’s degree in Statistics, Mathematics, Computer Science, Machine Learning, Economics, or any other related quantitative field. Working experience of the equivalent is also acceptable for this position.
• Masters in Data Science/Math or any Quantitative discipline is an added advantage
• Professional:
• Big Data or Data Science certification from recognized institutions
• Cloud and/or AI certifications from recognized institutions

Desired work experience:

• Must have a minimum of 6 years with proven experience in credit risk modeling and scorecard development in a banking or financial services environment.
• Hands-on experience with scorecards I.e. application, behavioral and collection scorecards
• Strong knowledge of statistical and machine learning methods (logistic regression, decision trees, gradient boosting, neural networks).
• Proficiency in Python, SQL or SAS, R.
• Prior experience in model governance and validation
• Demonstrated ability to work independently and deliver within tight timelines.