Senior Data Scientist, Vice President - Corporate Functions Technology
Listed on 2026-03-03
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IT/Tech
Data Scientist, Machine Learning/ ML Engineer, AI Engineer, Data Analyst
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 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 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.
Preferred Qualifications
- 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, scikit‑learn, Tensor Flow, PyTorch)
- Solid…
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