Role: Data Analyst - Min - 6 years of Exp

Key Responsibilities:

1. Strategic Leadership & Roadmap Development

• Define the Vision: Design and execute the long-term data analytics strategy, for proactive Continuous Monitoring.
• Methodology Evolution: Lead the transition toward full-population testing by embedding analytics into the risk assessment phase.
• Governance Framework: Strengthen data governance framework to ensure the integrity, security, and accuracy of data used in audit reporting, ensuring all workflows are documented for regulatory reliance.

2. Advanced Analytics & Tech Stack (Python, SQL, Power BI)

• Advanced Modeling (Python): Oversee the development of complex models using Python or R. Utilize predictive analytics, statistical sampling, and machine learning algorithms to anticipate emerging risks rather than just reporting on past events.
• Data Extraction & Manipulation (SQL): leverage advanced SQL scripting to query enterprise data warehouses directly.
• Visualization & Storytelling (Power BI): Architect dynamic executive dashboards using Power BI that translate complex datasets into intuitive visual stories, allowing stakeholders to "self-serve" risk insights.
• Fraud Detection: Architect sophisticated fraud detection scenarios and behavioral analysis models to identify anomalies across financial and operational datasets.

3. Stakeholder Management & Business Impact

• Translating Data to Value: Act as the bridge between technical data teams and business stakeholders. You must articulate complex findings into clear, actionable business insights for the Audit Committee.
• Business Acumen: Apply deep business knowledge to analytics. You must understand how the business generates revenue and where operational risks lie to ensure models are commercially relevant, not just theoretically correct.
• Cross-Functional Collaboration: Partner with internal teams to leverage existing data lakes and align on architecture.

4. Team Leadership & Quality Assurance

• Mentorship: Manage and mentor a team of data analysts and auditors. Foster their technical growth in SQL querying, Python scripting, and Power BI dashboarding.
• Quality Control: Ensure all analytical deliverables meet rigorous documentation standards. Validate code logic and query integrity to ensure results are accurate and suitable for external audit reliance.