Who We Are Looking For

We are seeking a Senior Data Scientist, Vice President to design and deliver advanced, data driven AI solutions supporting our Internal Audit Functions. In this hands‑on role, you will build the most relevant solutions leveraging a variety of methods including statistical models, machine learning, generative AI and agentic AI to drive measurable benefits and outcomes in a regulated enterprise audit environment.

This is a senior individual‑contributor role with end‑to‑end accountability for delivery. You will serve as a senior technical leader and subject‑matter expert, partnering closely with auditors, data engineers, solution architects, product managers, and change specialists to embed AI into redesigned audit workflows in a way that enhances auditor effectiveness and meets enterprise and regulatory standards.

What You Will Be Responsible For

Solution Design & Strategy

• Lead the end-to-end design of advanced analytics and AI solutions for complex, high-impact audit challenges, translating ambiguous business problems into scalable, data-driven approaches.
• Define solution architecture and strategic direction, evaluating multiple design patterns (analytical, ML, GenAI) to ensure alignment with audit objectives, enterprise standards, and long-term sustainability.
• Drive rapid prototyping and iterative delivery, incorporating structured stakeholder feedback loops while maintaining clear product vision and prioritization discipline.
• Optimize trade-offs across speed, model performance, scalability, cost, and operational risk, ensuring solutions are production-ready and deliver measurable business value.

Model Strategy, Selection & Development

• Establish and own model selection frameworks, determining when to leverage existing models versus developing bespoke analytical or AI solutions (statistical, ML, SLM, LLM).
• Design, develop, and refine advanced models that generate actionable, audit-relevant insights, with strong emphasis on interpretability and decision usefulness.
• Set best practices for feature engineering, model architecture, and experimentation, guiding the team (formally or informally) on technical rigor and innovation.
• Clearly articulate, defend, and document modeling decisions to both technical and non-technical audiences, including senior stakeholders and control functions.

AI, Model Risk & Governance Leadership

• Act as the primary interface with Model Risk Management and governance functions, ensuring all solutions meet enterprise standards for validation, explainability, auditability, and lifecycle management.
• Embed “controls by design” into all models and solutions, proactively addressing regulatory expectations and audit scrutiny.
• Lead governance for GenAI/LLM solutions, including prompt strategy, output reliability, grounding, traceability, and human-in-the-loop controls.
• Anticipate emerging regulatory and risk considerations (e.g., AI risk, model bias, data lineage) and incorporate them into solution design and documentation.

Data Strategy, Quality & Model Lifecycle Management

• Define and lead data strategy for AI use cases, including sourcing, profiling, enrichment, and representativeness, in partnership with data governance and engineering teams.
• Architect robust evaluation, validation, and monitoring frameworks, including metric design, benchmarking, stress testing, and drift detection.
• Own the end-to-end model lifecycle, from development through deployment and ongoing performance management, ensuring sustained reliability and relevance.
• Establish scalable standards for data quality, feature reuse, and model monitoring across the portfolio of audit analytics solutions.

Stakeholder Influence & Enterprise Enablement

• Serve as a trusted advisor to Internal Audit, Technology, and senior leadership, shaping how AI and data science are applied within audit and risk management.
• Influence adoption and scaling of solutions beyond Corporate Audit by partnering with first and second lines of defense to identify enterprise-wide use cases.
• Drive alignment between analytics solutions and audit methodology, ensuring outputs are actionable, defensible, and embedded into audit workflows.
• Partner with change management and product teams to enable adoption through training, documentation, and integration into standard operating processes.

What We Value

The skills that will help you succeed in this role include:

• End-to-End Model Ownership in Regulated Environments — Demonstrated ability to lead the full lifecycle of complex models (design, development, validation, deployment, and monitoring), with deep expertise in explainability, auditability, and alignment to regulatory and model risk expectations.
• Risk-Focused Applied Machine Learning — Advanced capability in translating complex data patterns (trends, clusters, anomalies, outliers) into prioritized, decision-ready risk signals, with a strong understanding of audit and control frameworks to ensure outputs are actionable and defensible.
• Evaluation, Validation & Performance Oversight — Proven track record of designing fit-for-purpose evaluation frameworks, including metric strategy, benchmarking, stress testing, and robust validation techniques; deep expertise in error analysis, bias detection, and model drift management in production environments.
• Strategic Data Acumen — Strong data intuition with the ability to define data sourcing, profiling, and feature engineering strategies at scale; partners effectively with data engineering and governance functions to ensure high-quality, representative, and sustainable datasets.
• Responsible GenAI / LLM Expertise — Advanced experience developing and governing GenAI solutions, including prompt engineering, evaluation design, grounding techniques, and implementation of safeguards (traceability, human-in-the-loop controls, output validation) in line with enterprise AI risk standards.
• Technical Leadership & Engineering Excellence — Expert-level Python and advanced SQL skills, coupled with strong software engineering discipline to design scalable, reliable ML pipelines; sets standards for code quality, reproducibility, and maintainability across the data science lifecycle.
• Cloud-Native Machine Learning (AWS) — Deep experience architecting, developing, and deploying enterprise-grade ML solutions in AWS, including SageMaker, with a strong focus on scalability, automation, cost optimization, and integration into production ecosystems.
• Executive Communication & Influence — Exceptional ability to translate complex analytical concepts into clear, concise, and impactful insights; trusted advisor to senior stakeholders, influencing decisions and driving adoption by aligning outputs to audit methodology and business priorities.

Education & Preferred Qualifications

• Advanced Education in a Quantitative Discipline — Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Engineering, or a related field; advanced coursework or specialization in machine learning, AI, or statistical modeling strongly preferred.
• Deep, Progressive Experience in Applied Data Science — 7–10+ years of progressively senior experience in data science, machine learning, or advanced analytics, with a proven track record of delivering and operationalizing models in complex, regulated environments.
• Expert-Level Python & ML Ecosystem Proficiency — Advanced proficiency in Python and leading ML/AI libraries (e.g., pandas, scikit-learn, TensorFlow, PyTorch), with the ability to design robust, scalable, and production-grade analytical solutions.
• Strong Theoretical and Practical Foundations — Deep understanding of machine learning, statistical modeling, and software engineering principles, including model selection, tuning, validation, and reproducibility in production settings.
• Experience with Large-Scale Data Platforms — Strong experience working with SQL and distributed data processing frameworks (e.g., Spark, Databricks), including optimizing data pipelines and handling large, complex datasets.
• Cloud-Native ML Deployment Expertise (AWS) — Demonstrated experience architecting, deploying, and maintaining machine learning solutions in AWS (including SageMaker), with a focus on scalability, reliability, and cost efficiency.
• Experience Operating in Controlled / Regulated Environments — Proven ability to develop and deploy models within governance frameworks, with familiarity in model risk management, validation, and documentation standards.
• Executive-Level Communication & Influence — Exceptional ability to translate complex analytical concepts into clear, concise, and actionable insights, effectively influencing senior stakeholders and aligning outcomes to business and audit objectives.

Nice‑to‑Have Qualifications

• Experience Applying AI in Regulated Environments — Demonstrated experience designing and deploying analytics or AI solutions within Internal Audit, risk management, compliance, or other highly regulated industries, with a strong understanding of how outputs must align to control frameworks, audit standards, and regulatory expectations.
• Deep Understanding of Model Risk & Data Governance — Strong working knowledge of model risk management frameworks, data governance principles, and regulatory expectations (e.g., explainability, documentation, validation, lineage), with experience embedding these considerations into solution design and delivery.
• Advanced MLOps & Production Excellence — Experience implementing and operating mature MLOps practices, including CI/CD for ML pipelines, automated testing, model versioning, monitoring, and incident management, ensuring reliability and scalability of production AI systems.
• Enterprise-Grade LLM / NLP Application — Hands-on experience designing and deploying LLM and NLP solutions in enterprise contexts, including prompt engineering, evaluation design, grounding techniques, and integration into business workflows with appropriate governance and controls.
• AI-Driven Document & Image Intelligence — Experience building and scaling AI solutions for document and image processing (e.g., OCR, classification, entity extraction), with a focus on embedding these capabilities into end-to-end workflows that drive operational efficiency and decision-making.