Data Analyst

Purpose of the Role

To produce reliable business information and actionable insights through the collation, validation and analysis of data, enabling informed underwriting, pricing, claims and portfolio management decisions within the CAHTMAEB business cluster.

Key responsibilities:

Portfolio Analytics and Underwriting Insights:

• Produce and analyse portfolio reports covering premiums, claims, loss ratios, rate movements and other relevant performance indicators.
• Identify emerging trends, underwriting opportunities, claims latency, risk concentrations and data anomalies.
• Translate analytical findings into clear recommendations that support risk selection, pricing, underwriting actions and product reviews.
• Monitor exposure accumulations, concentration risks and applicable limit breaches.

Data Management and Quality:

• Develop and maintain reliable datasets for reporting, pricing and modelling.
• Perform regular data validation, reconciliation and quality testing.
• Identify root causes of data quality issues and work with relevant stakeholders to implement sustainable corrective actions.
• Maintain data definitions, business rules, process documentation and appropriate audit trails.

Reporting and Business Intelligence:

• Produce accurate and timely management information, portfolio reports and dashboards.
• Develop and maintain self-service reports and business intelligence solutions.
• Ensure that reports use consistent definitions, approved data sources and appropriate controls.
• Present complex findings clearly to technical and non-technical stakeholders.

Pricing, Modelling and Technical Support:

• Prepare and validate data for pricing, segmentation, forecasting and predictive modelling.
• Support the testing, implementation and monitoring of rates, underwriting rules and analytical models.
• Assist with system changes, data migrations, rate updates and user acceptance testing.
• Support exposure analysis and reinsurance treaty renewals, regulatory and governance reporting.

Process Improvement and Stakeholder Engagement:

• Work with underwriting, actuarial, claims, reinsurance, finance and technology teams to deliver practical data solutions.
• Automate repeatable data and reporting processes to reduce manual effort and operational risk.
• Respond to ad hoc analytical requests and data enquiries.
• Promote data standards and support users in adopting reports, dashboards and analytical tools.
• Participate in approved AI and automation initiatives, including testing, implementation and monitoring.
• Contribute to the development, deployment and responsible governance of data and AI solutions that improve decision making, operational efficiency and customer outcomes.
• Work with business and technology stakeholders to identify and prioritise AI use cases that support Santam’s strategic objectives, competitive advantage and long-term business performance.
• Support the ongoing monitoring of AI solutions for performance, data quality, explainability, fairness and compliance with approved governance, risk and ethical standards.

Qualifications and Experience

• At least two years’ relevant experience in data analysis, business intelligence, actuarial analysis, pricing analytics or a similar analytical role
• Experience extracting, reconciling and analysing data from multiple sources
• Experience developing reports or dashboards for business users
• Experience in short-term insurance, commercial insurance or casualty insurance
• Exposure to underwriting, pricing, claims, exposure management or reinsurance data
• Experience working with policy administration systems and data warehouses

Skills

• Advanced Excel, including data validation, reconciliation and analytical modelling
• Proficiency in SQL for data extraction, transformation and analysis
• Experience using a business intelligence and visualisation tool, preferably Power BI
• Strong data management and data quality skills
• Experience working with relational databases, data warehouses or similar analytical data environments
• Ability to translate business requirements into clear analytical outputs
• Understanding of the AI solution lifecycle, including use case identification, data preparation, testing, deployment, performance monitoring and governance.
• Ability to evaluate AI generated outputs critically and translate them into reliable, explainable and actionable business insights.
• Awareness of responsible AI principles, including data privacy, security, fairness, transparency, human oversight and model risk.
• Experience using AI enabled analytical, automation or productivity tools in a controlled business environment.
• Experience contributing to or driving the development, implementation and ongoing monitoring of machine learning, generative AI or other advanced analytical solutions.

Advantageous:

• Advanced Excel, including data validation, reconciliation and analytical modelling
• Proficiency in SQL for data extraction, transformation and analysis
• Experience using a business intelligence and visualisation tool, preferably Power BI
• Strong data management and data quality skills
• Experience working with relational databases, data warehouses or similar analytical data environments
• Ability to translate business requirements into clear analytical outputs
• Understanding of the AI solution lifecycle, including use case identification, data preparation, testing, deployment, performance monitoring and governance.
• Ability to evaluate AI generated outputs critically and translate them into reliable, explainable and actionable business insights.
• Awareness of responsible AI principles, including data privacy, security, fairness, transparency, human oversight and model risk.
• Experience using AI enabled analytical, automation or productivity tools in a controlled business environment.
• Experience contributing to or driving the development, implementation and ongoing monitoring of machine learning, generative AI or other advanced analytical solutions.