Credit Risk Data Scientist

Network Finance; Randburg; On-site; ZAR 650,000 - 950,000; Full time

Job summary

Network Finance is seeking a technically strong Credit Risk Data Scientist to join our growing risk analytics team. You will contribute to development, monitoring, and enhancement of credit risk models across the customer lifecycle.

This role offers exposure to a broad range of analytics, portfolio monitoring, automated reporting, and data-driven recommendations to influence business decisions. Ideal candidates have 3–6 years in data science or credit risk within banking or fintech and strong

Qualifications

• Degree in Statistics, Mathematics, Data Science, or a quantitative field.
• 3–6 years in Data Science, Credit Risk Analytics, or related roles.
• Experience in Banking/FinTech lending or credit risk environments; predictive models and scorecards.

Responsibilities

• Develop, enhance, and monitor credit risk models across the customer lifecycle.
• Perform portfolio analysis to identify trends, risk drivers, and improvement opportunities.
• Conduct exploratory data analysis, feature engineering, segmentation, and model monitoring.
• Support model validation, back-testing, calibration, and governance activities.
• Develop automated reports, dashboards, and recurring analytical outputs.
• Build BI dashboards to support portfolio monitoring and performance management.

Skills

Advanced SQL

Python

SAS

Predictive modelling

Statistical analysis

Portfolio analytics

Stakeholder engagement

Communication

Data storytelling

Education

Degree in Statistics or Mathematics or Data Science

Tools

Power BI

Tableau

SAS Studio

Job description

We are seeking a technically strong and analytically minded Credit Risk Data Scientist to join a growing risk analytics function. The successful candidate will contribute to the development, monitoring, and enhancement of credit risk models while supporting portfolio monitoring, reporting automation, and analytical investigations across the credit lifecycle.

This role offers exposure to a broad range of credit risk analytics activities and is ideal for someone who enjoys working with large datasets, uncovering meaningful insights, building predictive solutions, and influencing business decisions through data-driven recommendations.

Key Responsibilities

• Assist in the development, enhancement, and monitoring of credit risk models across the customer lifecycle.
• Perform in-depth portfolio analysis to identify trends, risk drivers, anomalies, and opportunities for performance improvement.
• Conduct exploratory data analysis, feature engineering, segmentation analysis, and model performance monitoring.
• Support model validation, back-testing, calibration, and ongoing model governance activities.
• Develop and maintain recurring analytical reports, dashboards, and automated reporting solutions.
• Build and enhance business intelligence dashboards to support portfolio monitoring and performance management.
• Extract, transform, validate, and reconcile large datasets from multiple data sources.
• Produce regular and ad hoc portfolio monitoring reports and analytical investigations.
• Support the optimisation of credit risk strategies through data-driven analysis and recommendations.
• Investigate data quality issues and support remediation initiatives.
• Collaborate with business stakeholders to translate requirements into analytical and reporting solutions.
• Contribute to process improvements, automation initiatives, and enhanced risk monitoring capabilities.

Experience and Skills Required

Education

• Degree in Statistics, Mathematics, Data Science, Computer Science, Engineering, Economics, Finance, Actuarial Science, or another quantitative discipline.

Experience

• Minimum 3-6 years' experience in a Data Science, Credit Risk Analytics, Quantitative Analytics, Modelling, or Risk Analytics role.
• Experience within Banking, Financial Services, Lending, FinTech, or Credit Risk environments.
• Proven experience working with predictive models, scorecards, portfolio analytics, or risk modelling frameworks.
• Experience analysing large and complex datasets to produce actionable business insights.
• Exposure to model monitoring, validation, performance tracking, and analytical investigations.
• Experience building and maintaining automated reporting and dashboard solutions.
• Strong understanding of credit lifecycle analytics and portfolio management principles.

Skills

• Advanced SQL skills for data extraction, transformation, and analysis.
• Strong programming capability in Python, SAS, or similar analytical tools.
• Experience developing dashboards using Power BI, Tableau, or similar visualisation platforms.
• Understanding of predictive modelling, statistical techniques, and quantitative analysis.
• Knowledge of credit risk concepts including scorecards, provisioning, portfolio monitoring, and model performance measurement.
• Strong analytical, problem-solving, and critical-thinking abilities.
• Ability to work with large datasets and derive meaningful insights from complex information.
• Strong stakeholder engagement and communication skills.
• Ability to clearly present technical findings to both technical and non-technical audiences.
• Experience automating reporting and analytical processes would be advantageous.