Lead-Data Analytics

Key responsibilities

• Execute full-population data testing to replace or complement traditional sampling, covering financial transactions, procurement cycles, project cost flows, contract management and operational data.

• Design and run structured analytics for individual audit assignments, including trend analysis, stratification, Benford's Law testing, duplicate detection, outlier identification and ratio analysis.

• Develop and maintain machine learning models for anomaly detection, fraud risk scoring, vendor risk ranking and audit universe risk prioritisation.

• Apply NLP techniques to analyse contracts, audit reports, management responses and regulatory documents to surface patterns and flag risks.

• Build predictive models that anticipate control failures based on historical audit findings, near-miss data and operational KPIs.

• Validate, document and version-control all models in accordance with the function's AI governance framework.

• Build and maintain Internal Audit's risk dashboards in Power BI / Tableau, providing real-time visibility of audit universe risk indicators for real estate projects and data centre operations.

• Design and operationalise a suite of continuous control monitoring (CCM) routines that run on a scheduled, automated basis across key financial and operational processes.

• Design and build data pipelines that extract, transform and load (ETL/ELT) data from source systems (ERP, project management, HRMS, CRM, billing platforms) into the Internal Audit analytics environment.

• Develop and maintain connections to structured data sources (SQL Server, Oracle, SAP) and unstructured sources (SharePoint, emails, PDFs) as required.

Qualifications

• Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering or a quantitative discipline; Master's degree strongly preferred.

• 6–10 years of experience in data analytics, data science or a closely related field, with at least 2 years in an audit, risk, finance or compliance environment.

• Expert proficiency in Python (pandas, NumPy, scikit-learn, statsmodels) and/or R for data analysis and modelling.

• Strong SQL skills- ability to write complex queries, optimise performance and work across multiple RDBMS platforms (SQL Server, Oracle, PostgreSQL).

• Hands-on experience with BI and visualisation tools like Power BI and/or Tableau, including data modelling (DAX, calculated fields) and dashboard publishing.

• Demonstrated experience building and deploying machine learning models (classification, regression, clustering, anomaly detection).

• Experience with ETL/ELT processes and data pipeline development (Azure Data Factory, dbt, Airflow or similar).

• Proficiency in working with large, complex, multi-source datasets and resolving data quality issues.