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 handson role, you will apply statistical modeling, machine learning, and responsible AI to drive riskbased audit planning, continuous risk monitoring, and actionable insights in a regulated enterprise environment.
This is a senior individualcontributor role with endtoend accountability for model development, governance, and delivery. You will serve as a senior technical leader and subjectmatter 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 Development
- 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 decisionmaking.
- Apply predictive analytics and historical audit data to enable riskbased audit planning and continuous risk monitoring.
AI & Model Governance
- 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 LLMbased solutions, including prompt design and output evaluation, ensuring results are grounded, traceable, and subject to appropriate human review.
Data Quality, Evaluation & Monitoring
- Own feature engineering and data profiling strategies, partnering with data engineering to curate highquality, 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 & Enablement
- Partner with Internal Audit and Technology stakeholders to align analytics with audit methodology and realworld needs.
- Translate complex analytical results into clear, actionable insights for nontechnical audiences.
- Support adoption through documentation, training, and integration into audit workflows with defined review checkpoints.
What We Value
The skills that will help you succeed in this role include:
- Endtoend model delivery - ability to build, validate, deploy, and monitor models with clear explainability and auditability in a regulated environment.
- Riskfocused 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 fitforpurpose 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.
- Handson technical excellence - expert Python skills, strong software engineering practices for reliable ML/data pipelines, solid SQL, and experience with enterprisescale data tooling.
- Cloudfirst 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 nontechnical stakeholders.
Education & Preferred Qualifications
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Engineering, or a related quantitative field
- 7+ years of handson 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, scikitlearn, TensorFlow, PyTorch)
- Solid foundation in machine learning, statistical modeling, and software engineering best practices, including model tuning and validation
- Experience working with SQL and largescale data platforms (e.g., Spark, Databricks)
- Handson experience developing and deploying models in AWS, particularly Amazon SageMaker
- Proven ability to communicate complex analytical concepts to nontechnical stakeholders, including senior leaders
NicetoHave Qualifications
- Experience 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, an