Senior Data Scientist, Vice President - Corporate Functions Technology

We are seeking a Senior Data Scientist, Vice President to design and deliver advanced analytics and machine learning solutions supporting our Internal Audit Functions. In this hands‑on role, you will apply statistical modeling, machine learning, and responsible AI to drive risk‑based audit planning, continuous risk monitoring, and actionable insights in a regulated enterprise environment. This is a senior individual‑contributor role with end‑to‑end accountability for model development, governance, and delivery.

You will serve as a senior technical leader and subject‑matter expert, partnering closely with audit, data engineering, and architecture teams to embed analytics and AI into audit workflows in a way that enhances auditor effectiveness and meets enterprise and regulatory standards.

• Design, build, and refine statistical and machine learning models to identify risk patterns such as trends, clusters, outliers, and anomalies.
• Generate ranked risk signals and insights to support auditor review, prioritization, and decision‑making.
• Apply predictive analytics and historical audit data to enable risk‑based audit planning and continuous risk monitoring.

• Ensure all models meet enterprise standards for explainability, validation, auditability, and ongoing performance monitoring, with clear documentation of intended use and limitations.
• Lead the design and build of GenAI and LLM‑based solutions, including prompt design and output evaluation, ensuring results are grounded, traceable, and subject to appropriate human review.

• Own feature engineering and data profiling strategies, partnering with data engineering to curate high‑quality, representative datasets.
• Design and operate robust model evaluation and monitoring frameworks, including metric selection, validation, error analysis, drift detection, and ongoing performance tracking.

• Partner with Internal Audit and Technology stakeholders to align analytics with audit methodology and real‑world needs.
• Translate complex analytical results into clear, actionable insights for non‑technical audiences.
• Support adoption through documentation, training, and integration into audit workflows with defined review checkpoints.

• End‑to‑end model delivery — ability to build, validate, deploy, and monitor models with clear explainability and auditability in a regulated environment.
• Risk‑focused applied machine learning — skill in identifying patterns (trends, clusters, outliers, anomalies) and translating them into ranked, reviewable risk signals.
• Rigor in evaluation and monitoring — experience defining fit‑for‑purpose metrics, running thorough validations, performing error analysis, and implementing drift detection and ongoing performance tracking.
• Strong data instincts — emphasis on data profiling, feature engineering, and data quality, with close partnership with engineering to curate representative datasets.
• Responsible GenAI / LLM development — ability to iterate prompts and evaluation approaches while ensuring outputs are grounded, traceable, and subject to appropriate safeguards and human review.
• Hands‑on technical excellence — expert Python skills, strong software engineering practices for reliable ML/data pipelines, solid SQL, and experience with enterprise‑scale data tooling.
• Cloud‑first ML execution (AWS) — experience developing and deploying machine learning solutions in AWS, particularly using Amazon Sage Maker.
• Stakeholder partnership and communication — ability to translate complex analytics into clear, actionable insights aligned to audit methodology and usable by non‑technical stakeholders.

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Engineering, or a related quantitative field.
• 7+ years of hands‑on experience in data science, machine learning, or advanced analytics, including deploying models into production.
• Strong proficiency in Python and common ML/data libraries (e.g., pandas,…