Credit Risk Analyst

Network Recruitment; Johannesburg; On-site; ZAR 800,000 - 1,100,000; Full time

Job summary

Network Recruitment is seeking a Credit Risk Data Scientist to join a leading financial services organisation in Johannesburg. You will develop and monitor credit risk models, perform portfolio analytics, and automate reporting in a data-driven environment.

The ideal candidate has 3–6 years of experience in data science or risk analytics, strong SQL and Python skills, and experience with Power BI/Tableau. You will work with large datasets to support risk decisions and governance.

Qualifications

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

Responsibilities

• Develop, enhance, and monitor credit risk models across the customer lifecycle.
• Perform portfolio analysis to identify trends, risk drivers, and opportunities for performance improvement.
• Conduct exploratory data analysis, feature engineering, segmentation analysis, and model performance monitoring.
• Support model validation, back-testing, calibration, and governance activities.
• Develop and maintain recurring analytical reports, dashboards, and automated reporting solutions.
• Build BI 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 analyses.
• Support the optimisation of credit risk strategies through data-driven analysis.
• Investigate data quality issues and remediation initiatives.
• Collaborate with business stakeholders to translate requirements into analytical solutions.
• Contribute to process improvements, automation initiatives, and enhanced risk monitoring capabilities.

Skills

Analytical thinking

Stakeholder engagement

Communication

Problem solving

Education

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

Tools

SQL

Python

SAS

Power BI

Tableau

Excel

Job description

Our client is a leading financial services organisation operating within a data-driven and analytical environment. They are committed to leveraging advanced analytics, technology, and data science to enhance customer outcomes, optimise risk management strategies, and drive sustainable business growth.

This is an exciting opportunity to join a high-performing analytics team where you will play a key role in credit risk modelling, portfolio analysis, reporting automation, and strategic decision support.

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.