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# Lead, Data Science Internal Audit

- **Company:** AbbVie Inc.
- **Location:** Plainfield, NJ, United States
- **Experience:** Expert
- **Salary:** $96,500.0 - $183,500.0
- **Contract:** Permanent contract
- **Skills:** Data Analysis, R (Programming Language), Python (Programming Language), Regression Analysis, Natural Language Processing, Power BI, SQL Databases, Tableau (Software), Multivariate Regression, Information Technology, Data Analytics, Data Pipelines
- **Published:** August 10, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=417271279446f91c

## About the Role

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

## Description

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.


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