Lead, Data Science Internal Audit
Core
Design and implement advanced statistical models and NLP techniques to proactively identify risks, uncover operational improvement opportunities, and support objective decision-making across the enterprise by analyzing enterprise datasets.
Role type
Lead Data Scientist (Internal Audit)
Builds
Population-level data analytics frameworks, predictive models, classification algorithms, automated data pipelines, and monitoring dashboards.
Domain
Healthcare / Internal Audit / Risk Management
Deliverable
production ML models | dashboards & analysis
Required skills
Python, SQL, R, statistical modeling, natural language processing (NLP), multivariate regression, decision trees, ensemble methods, data pipeline development, dashboard creation, risk indicator definition, mentorship
Preferred skills
MBA or Master's in relevant field, PowerBI/Tableau experience
Technologies
Python, SQL, R, PowerBI, Tableau
Responsibilities
Design and implement advanced statistical models including decision trees, regression analyses, and NLP techniques to identify anomalies and risk indicators. Develop and maintain population-level data analytics frameworks for ad-hoc audit testing and continuous monitoring. Build predictive models and classification algorithms to assess risk profiles and prioritize audit focus areas. Apply NLP techniques to analyze unstructured data sources to detect compliance issues or fraudulent activities. Create automated data pipelines and monitoring dashboards to enable real-time detection of control failures. Mentor junior analytics team members on data science techniques and audit analytics applications. Present complex analytical findings to audit leadership and business stakeholders in clear, actionable formats.
Seniority
Lead, hands-on IC with mentorship responsibilities