Senior Data Analytics Specialist at iOCO

What you’ll do:

Strategy and Planning:

• Support the Data Analytics Manager in developing an audit/advisory plan.
• Deliver on the internal audit strategic objectives, specifically those involving continuous auditing and monitoring, as well as 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 data sources and secure receipt of data files from the business via multiple channels.
• Creation of a data dictionary including definitions and availability (where appropriate).
• Build and maintain common data sets regularly required by audit delivery teams.
• Assist with data cleaning and preparation for audit delivery teams.
• Build and maintain a library of re-usable automated auditing programmes and functional specifications.
• Develop sustainable and re-useable Data Analytics models, programs and dashboards to improve the efficiency of Group Internal Audit and to improve audit coverage.
• Support audit delivery teams with the development of complex/insightful data analytics tests.
• Prepare data analytics audit programs that are aligned to the Risk and Control Matrices of the assigned audits.
• Ensure the quality and integrity of data used in analytics projects.
• Execute data analytics fieldwork using relevant tools such as Python and SQL.
• Design dashboards to visualise trends and insights using tools such as Power BI.
• Develop data analytics scripts for continuous monitoring purposes.
• Review own working papers for quality and completeness before sending them to the Audit Manager for review.
• Document findings for discussion with auditee/management, as well as validate exceptions for factual correctness.
• Take responsibility for clearing and finalising all review notes and queries.
• Manage client interaction for allocated areas of scope.
• Stay abreast of industry trends and advancements in data analytics and audit technology, as well as new regulatory pronouncements.
• Track audit process status for allocated section and effectively communicate any anticipated challenges, delays, etc.

Stakeholder Engagement:

• Collaborate with audit teams to integrate data analytics into the audit workflow.
• Build mutually beneficial relationships with internal and external stakeholders.
• Practice effective communication skills.
• Ensure a thorough understanding of the business environment, strategies, challenges and risks as well as prevailing legislation and emerging industry trends.

Financial Management:

• Effectively adhere to the budget (schedule, cost and scope) applicable to projects, as well as areas of responsibility.

Human Resources:

• Complete the necessary training as agreed in IDP (Individual Development Programme).
• Take responsibility for own career and performance management.
• Contribute to the social committee and attend social activities.
• Expected strategic/operational involvement: 20%/80%

Other

Your expertise:

• Minimum 5 years of data analytics experience, preferably within an internal audit or financial services environment.
• Proven experience with Python for Data Analysis, Automation and Machine Learning.
• Experience with data visualisation tools (e.g., Power BI, Tableau, QuickSight, etc.).
• Financial services knowledge and/or experience, with insurance industry experience preferred.
• Strong proficiency in Python and its libraries (e.g., pandas, NumPy, scikit-learn).
• Advanced understanding of database structures.
• 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 practical knowledge of data mining, data analysis and visualisation techniques.
• Proven track record of high performance in previous roles.
• Ability to translate complex data into actionable insights for non-technical audiences.
• Team player, able to collaborate and support peers and colleagues.

Qualifications required:

• A relevant degree or diploma in IT, Data Science, Statistics, or Audit is necessary.
• Relevant professional qualifications or certifications in Python, SQL, Power BI, machine learning, R, etc.