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.
