AI Model Governance and Validation Specialist (m/f/d)

Technologies we use

About the project

This is how we organize our work

This is how we work

Your responsibilities

• Continuously develop and enhance the risk management framework in line with advances in AI/ML technologies, including Agentic AI, while taking into account evolving regulatory requirements.
• Drive the implementation of AI governance and model risk standards in close collaboration with use case owners, model developers, and other stakeholders, helping to foster a strong risk-aware culture across the organization.
• Understand business processes and use cases to assess key risks from use of AI techniques, including bias, fairness, explainability, and model limitations. Identify control gaps and recommend appropriate risk mitigation measures.
• Plan and perform independent validations of AI/ML use cases, or provide expert support and guidance to business units in their validation activities. Review technical documentation, evaluate model implementation and monitoring processes, prepare validation reports, communicate findings, recommend remediation actions, and provide clear executive-level summaries.
• Support the effective inventory management of AI systems and use cases, ensuring timely and accurate model risk reporting.

Our requirements

• A solid understanding of banking, financial services, or another regulated industry. Knowledge of financial products and the role of models in key banking processes (e.g., ICAAP, ILAAP, valuation, credit decision-making, and compliance) would be an advantage.
• Knowledge of AI/ML techniques, including decision trees, random forests, gradient boosting, neural networks, and related methodologies.
• Familiarity with existing AI/ML regulations (e.g. EU AI Act) and their application and appreciation of developing regulations within the finance industry.
• Strong analytical and technical skills, including proficiency in Python, R, SQL, or equivalent tools, as well as hands-on experience with data analysis.
• Familiarity with model risk concepts and an understanding of the unique risks associated with AI/ML models. Previous experience in model risk management, model development, or model validation would be considered a strong asset.
• A university degree in Computer Science, Mathematics, Statistics, Data Science, Econometrics, Engineering, Physics, or another quantitative discipline.

Benefits