Lead, Data Science Internal Audit

Responsibilities:
- Design and implement advanced statistical models (decision trees, regression analyses, NLP) to identify anomalies, patterns, and risk indicators across enterprise datasets.
- Develop and maintain population-level data analytics frameworks for ad-hoc audit testing and continuous monitoring.
- Collaborate with internal audit teams to translate audit objectives into analytical approaches aligned with audit standards and compliance requirements.
- Build predictive models and classification algorithms (decision trees, ensemble methods) to assess risk profiles and prioritize audit focus areas.
- Apply NLP to analyze unstructured data (e.g., contracts, emails, transaction narratives) to detect compliance issues or fraudulent activities.
- Perform multivariate regression analyses to identify relationships between variables and quantify risk factors.
- Create automated data pipelines and monitoring dashboards for real-time detection of control failures or unusual transaction patterns.
- Partner with stakeholders to define key risk indicators and statistical thresholds for continuous monitoring alerts.
- Mentor junior analytics team members on data science techniques and audit analytics applications.
- Present findings to audit leadership and business stakeholders in clear, actionable formats.

Qualifications:
- 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.

Benefits (as stated):
- Paid time off (vacation, holidays, sick), medical/dental/vision insurance, 401(k); eligible for short-term incentive programs.