The purpose of the Data Analytics, Internal Audit team is to enhance the value and impact of Internal Audit at AbbVie by leveraging analytics and data-driven insights to proactively identify risks, uncover opportunities for operational improvement, and support informed, objective decision-making across the enterprise.

• Design and implement advanced statistical models including decision trees, regression analyses, and natural language processing (NLP) techniques to identify anomalies, patterns, and risk indicators across enterprise datasets.
• Develop and maintain population-level data analytics frameworks to support both ad-hoc audit testing requests and ongoing continuous monitoring initiatives.
• Collaborate with internal audit teams to translate audit objectives into analytical approaches, ensuring data science methodologies align with audit standards and compliance requirements.
• Build predictive models and classification algorithms using decision trees and ensemble methods to assess risk profiles and prioritize audit focus areas.
• Apply NLP techniques to analyze unstructured data sources (e.g., contracts, emails, transaction narratives) to detect potential compliance issues or fraudulent activities.
• Perform multivariate regression analyses to identify relationships between variables and quantify risk factors across business processes.
• Create automated data pipelines and monitoring dashboards to enable real-time detection of control failures or unusual transaction patterns.
• Partner with cross-functional stakeholders to define key risk indicators and develop statistical thresholds for continuous monitoring alerts.
• Mentor junior analytics team members on data science techniques, statistical best practices, and audit analytics applications.
• Stay current with emerging data science technologies and audit analytics trends to continuously enhance the team's analytical capabilities.
• Present complex analytical findings to audit leadership and business stakeholders in clear, actionable formats that support audit conclusions and recommendations.