Data Science Advisor

Responsibilities

• Partner across Audit, Technology, and platform teams to build a unified Audit platform with reusable data, analytics, automation, GenAI services, model operations, secure delivery, and enterprise controls.
• Develop advanced analytics, AI, and data science solutions to solve complex business and technical challenges and shape technical direction.
• Design, test, and validate audit solutions using advanced data science methods aligned with audit standards and methodology.
• Apply data science to improve risk measurement, valuation, decision‑making, and business performance.
• Create technical strategies and executive‑ready materials that communicate high‑impact solutions to leaders and stakeholders.
• Provide thought leadership on applying advanced analytics and data science to business challenges.
• Build solutions for continuous monitoring, risk detection, automated evidence generation, and deeper insights.
• Assess model effectiveness and fitness for use, ensure testing and monitoring, and explain key drivers and limitations.
• Lead cross‑functional teams through the model lifecycle, aligning changes with business goals.
• Stay current on industry practices, regulations, and internal standards to ensure compliance and elevate issues as needed.
• Advise senior leaders on priorities, balancing accuracy, speed, cost, and governance.
• Support the Model Owner and Lead Model User in building consensus, prioritizing requirements, testing changes, resolving findings and sharing best practices.
• Drive continuous improvement in modeling and analytics while promoting accountability, transparency, and proactive model risk management.
• Represent the Analytics team in internal forums, regulatory settings, and industry conferences, sharing best practices and thought leadership.

• 6 years of related experience in data science, machine learning, and AI solution development, including GenAI workflows.
• Master's degree in Data Science, Economics, Mathematics, Statistics, Computer Science, or a related field.
• Advanced proficiency in Python and core data science and machine learning techniques.
• Experience building end‑to‑end data science solutions using AWS data services such as Redshift, Athena, S3, and AWS Data Wrangler.
• Strong analytical skills to support testing, validation, model assessment, and business decision‑making.
• Strong communication skills, including the ability to explain technical concepts and solutions to non‑technical partners and stakeholders.
• Shows curiosity and adaptability in learning and responsibly applying new technologies, including artificial intelligence, to reimagine how we work.
• PhD in Data Science, Economics, Mathematics, Statistics, Computer Science, or a related field, or equivalent additional experience is desired.
• Experience in Internal Audit, Risk Management, Model Risk Management, or other highly regulated environments is desired.
• Experience applying advanced data science methods such as regression, SVM (support vector machines), random forests, and neural networks is desired.
• Strong technical writing, presentation, and executive stakeholder communication skills are desired.
• Proven ability to influence and collaborate across cross‑functional teams, including Audit, Technology, AI, and Risk partners is desired.
• Experience with AI engineering tools and patterns, such as Anthropic or OpenAI models and tool‑calling frameworks is desired.