Senior Data Analytics Specialist at Hollard Insurance

Purpose:

• The Senior Specialist: Data Analytics is responsible for providing data-driven insights and analytics support to the Group Internal Audit team. The role involves analysing multiple data sources to identify trends, anomalies and areas of risk across Hollard, its subsidiaries and key partners. The specialist works closely with audit teams to improve audit efficiency and effectiveness through analytical tools and techniques. The specialist uses approved AI-enabled analytics capabilities to accelerate data preparation, highlight anomalies and strengthen risk targeting, while applying professional judgement and adhering to the Internal Audit methodology. This role also supports the advancement of the analytics capability through defined strategic initiatives.

Key Objectives:

Strategy and Planning:

• Support the Data Analytics Manager in developing the analytics audit/advisory plan.
• Contribute to Internal Audit strategic objectives, including continuous auditing/monitoring and data-driven insights and reporting.

Operational / Technical:

• Develop a thorough understanding of the overarching risk universe, processes and associated internal controls, issues, pertinent regulations and risks associated with the engagement scope.
• Arrange access to approved data sources and securely obtain required data extracts from the business.
• Prepare data analytics audit programs that are aligned to the Risk and Control Matrices of the assigned audits.
• Ensure the quality, completeness and integrity of data used in analytics activities.
• Execute data analytics fieldwork using relevant tools such as ACL, SQL, and Python.
• Develop and maintain data analytics scripts and tests for continuous monitoring purposes.
• Create and maintain a data dictionary (where appropriate) including definitions, lineage and availability.
• Develop sustainable and reusable analytics models, scripts and dashboards to improve audit coverage and the efficiency of Group Internal Audit.
• Apply approved AI-enabled analytics capabilities (e.g., anomaly detection, exception prioritisation and automated summaries) to enhance testing efficiency and risk coverage, and document prompts, assumptions and validation performed.
• Review own working papers for quality and completeness prior to submission for review.
• Document analytics results for discussion with management and validate exceptions for factual accuracy.
• Design dashboards to visualise trends and insights.
• Take accountability for clearing and finalising all review notes and queries.
• Manage stakeholder interaction for allocated areas of responsibility.
• Stay abreast of industry trends and developments in data analytics, audit technologies, and regulatory pronouncements.
• Track progress against audit timelines and proactively communicate anticipated challenges and delays.

Stakeholder Engagement:

• Collaborate with the Group Internal Audit teams to integrate data analytics effectively into the audit lifecycle.
• Build and maintain professional relationships with internal and external stakeholders.
• Working closely with data engineering/BI teams, data stewards, and system owners.
• Communicate analytics results clearly and effectively, including to non-technical audiences.
• Maintain a sound understanding of the business environment, strategies, challenges, risks, and emerging industry trends.

Financial Management:

• Adhere to the budgets, timelines, scope and cost constraints for allocated work.

Human Resources:

• Complete required training as agreed in Individual Development Programme.
• Take ownership of personal performance and career development.
• Live the Hollard Way and contribute positively to team culture and initiatives.
• Contribute to the social committee and attend social activities.

Required Knowledge and Experience    
Required Experience:

• Minimum of 5 years data analytics experience, preferably within internal audit or financial services.
• Proven Python capability for data analysis, automation and responsible use of AI-enabled features.
• Experience with data visualisation tools (e.g., Power BI, Tableau, QuickSight).

Knowledge 

• Financial services knowledge and/or experience, with insurance industry experience preferred.
• Good understanding of machine learning algorithms and techniques.
• Knowledge of the business environment and value-chain.
• Understanding of internal audit standards, methodology and best practice.
• Basic knowledge of data governance principles and frameworks (e.g. DAMA).
• Advanced understanding of database structures.
• Advanced practical knowledge of data mining, data analysis and visualisation techniques.
• Basic understanding of system architecture and data flows.
• Foundational understanding of AI enabled analytics and automation concepts, including limitations, bias awareness and appropriate validation within an audit context.

Skills

• Proficiency in data analytics tools (e.g., ACL, SQL, SAS, IDEA, Python, R) and working knowledge of at least one data visualisation tool (e.g., QuickSight, Power BI). Experience with ACL is an advantage.
• Strong scripting capability to develop and maintain repeatable analytics tests.
• Ability to translate complex analytical results into clear, actionable insights for non-technical audiences.
• Ability to use approved AI assistants to accelerate routine analytics tasks, such as query drafting and documentation, and to independently validate outputs before relying on them as audit evidence.
• Strong planning and time-management skills.
• Effective written and verbal communication skills.
• Strong analytical and problem-solving ability.
• High attention to detail and commitment to quality.
• Proven track record of high performance in previous roles.
• Ability to work independently and collaboratively within a team environment.

Educational Requirements    

Required Qualifications 

• Relevant degree/diploma in information technology, data science, statistics, mathematics, engineering or auditing.
• One or more of the following (or related) data/IT related certifications:
• ACL (or equivalent) certification.
• SQL certification; and
• Python certification (or demonstrable proficiency).
• Not essential, but beneficial qualifications
• Microsoft Power BI related certifications
• Google or IBM Data Analytics Professional

QuickSight certifications

• Certified Information Systems Auditor (CISA)
• Certified Internal Auditor (CIA) / Professional Internal Auditor (PIA)

Deadline:27th April,2026