Lead, Data Science Internal Audit

AbbVie•North Chicago, IL

20h

About The Position

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.

Requirements

Bachelor’s degree in Computer Science, Statistics, Data Science or Related Field
Minimum 6+ years of progressive and related experience.
Proficiency in Python, SQL, R, PowerBI/Tableau experience preferred

Nice To Haves

MBA/Master's in relevant field preferred

Responsibilities

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.