Senior Manager (Audit Analytics, Automation & Innovation)

Audit Analytics, Automation & Innovation

• Lead Internal Audit's data analytics, automation, and technology innovation initiatives, including the ongoing development and execution of the department's technology roadmap.
• Design, build, and maintain SQL-based analytical models to support audit engagements — including population testing, anomaly detection, trend analysis, and control effectiveness assessment.
• Analyse data to identify and implement analytics, automation, and reporting solutions that provide meaningful insight and improve the effectiveness of audit planning and execution across the audit universe.
• Embed analytics into audit methodology and related processes, including risk assessment, audit planning, scoping, fieldwork, exception analysis, issue impact quantification, corrective action validation, and reporting.
• Drive automation opportunities that streamline audit procedures, improve consistency, and reduce manual effort across Internal Audit activities.
• Transform raw data from multiple sources (data warehouse, blockchain, internal systems) into audit-ready datasets and quantified findings.
• Provide data-driven evidence and quantitative analysis to support audit conclusions, replacing or supplementing traditional sample-based testing with full-population analytics.

Operational Risk & Fraud Risk Radar

• Collaborate with Business Audit Director to design and operate the Fraud Risk Radar programme — a proactive fraud surveillance framework that detects suspicious employee wallet activity, kickback patterns, dual employment signals, and affiliate commission manipulation in near real-time.
• Develop and maintain continuous monitoring dashboards and automated alert systems to detect control failures, policy violations, and emerging operational risks.
• Define monitoring rules and thresholds in collaboration with audit leads, calibrated to the company's risk appetite and operational context.
• Investigate and triage alerts, escalating confirmed exceptions to audit or investigation teams with supporting data packages.
• Support forensic and ad-hoc investigations with rapid data extraction, pattern analysis, and evidence packaging.

AI & Advanced Analytics

• Apply advanced analytical techniques (anomaly detection, clustering, predictive modelling) to identify patterns, outliers, and emerging risks across business operations.
• Explore and implement AI/ML-driven tools to enhance audit efficiency — including natural language processing for contract/document review, and automated risk scoring.
• Stay current with developments in data engineering, AI, and blockchain analytics to continuously evolve the team's analytical capability.

Data Management, Reporting & Technology Enablement

• Design and develop processes to extract data from internal and external sources, transform the data into meaningful business and audit-relevant elements, combine data across sources, and load data into appropriate data structures for analysis and reporting.
• Support audit teams through onboarding new data sets, maintaining existing data sets, and enabling continued access to relevant systems, dashboards, and reporting solutions.
• Develop metrics, key risk indicators, key performance indicators, dashboards, and other reporting solutions to make data-driven insights available to audit teams and leadership.
• Provide production support for data-related processes to ensure business continuity, performance, and integrity of reports, dashboards, automations, and other data solutions.

Business Partnership & Collaboration

• Partner with audit leads to translate business risks and audit objectives into data requirements and analytical approaches.
• Work collaboratively with IT teams, internal business partners and audit teams to identify, understand, implement, and support new technology capabilities while maintaining alignment to Internal Audit objectives.
• Maintain and enhance analytics that support business audits, nvestigations, compliance audits, and IT audits.
• Work with Data Engineering and IT teams to secure data access, ensure data quality, and maintain pipeline reliability.

Requirements

• Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Finance, or related quantitative field.
• Minimum 6 years of experience in data analytics, with at least 2 years supporting audit, risk, compliance, or financial control functions.
• Proficient in SQL (required); experience with Python/R for statistical analysis preferred.
• Hands-on experience building dashboards and visualisations.
• Familiarity with data warehouse architectures (Hive, StarRocks, Spark, or similar).
• Experience with or strong interest in blockchain data analysis, on-chain forensics, or crypto-specific analytics tools is a plus.
• Understanding of internal audit methodology and control frameworks (COSO, IIA standards) preferred; willingness to learn if from a pure data background.
• Strong problem-solving skills with the ability to translate ambiguous business questions into structured analytical approaches.
• Effective communication skills — able to present complex data findings in clear, audit-report-ready language for non-technical stakeholders.
• Proficient in Chinese and English for business usage.
• CIA, CISA, or data-related certifications (e.g., Google Data Analytics, AWS Data Analytics) are a plus but not required.