Who We Are Looking For 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.What You Will Be Responsible For Model DevelopmentDesign, 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.AI & Model GovernanceEnsure 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.Data Quality, Evaluation & MonitoringOwn 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.Stakeholder Partnership & EnablementPartner 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.What We ValueEnd‑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 SageMaker.Stakeholder partnership and communication — ability to translate complex analytics into clear, actionable insights aligned to audit methodology and usable by non‑technical stakeholders.Education & Preferred QualificationsBachelor’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, scikit‑learn, TensorFlow, PyTorch).Solid foundation in machine learning, statistical modeling, and software engineering best practices, including model tuning and validation.Experience working with SQL and large‑scale data platforms (e.g., Spark, Databricks).Hands‑on experience developing and deploying models in AWS, particularly Amazon SageMaker.Proven ability to communicate complex analytical concepts to non‑technical stakeholders, including senior leaders.Nice‑to‑Have QualificationsExperience applying analytics or AI in Internal Audit, risk management, compliance, or other regulated industries.Familiarity with model risk management, data governance, and regulatory expectations.Exposure to MLOps practices such as CI/CD, model monitoring, and production support.Hands‑on experience with LLMs or NLP in enterprise use cases.Benefits Inclusive development opportunities, flexible work‑life support, paid volunteer days, and vibrant employee networks that keep you connected to what matters most.As an Equal Opportunity Employer, we consider all qualified applicants for all positions without regard to race, creed, color, religion, national origin, ancestry, ethnicity, age, disability, genetic information, sex, sexual orientation, gender identity or expression, citizenship, marital status, domestic partnership or civil union status, familial status, military and veteran status, and other characteristics protected by applicable law.#J-18808-Ljbffr