Senior Associate - Model Validation and AI Governance

• Model methodology review: Assess model design choices, data sources, feature engineering, and performance metrics, providing constructive challenge where necessary
• Independent validation: Critically evaluate both traditional statistical models and modern AI approaches by replicating results, testing alternatives, and, where appropriate, developing challenger models to assess risk and impact
• Reporting and documentation: Produce detailed validation reports alongside clear, accessible summaries for non-technical audiences, using concise language and effective visuals
• Regulatory interpretation: Analyse technical documentation and regulatory guidance, converting requirements into practical validation frameworks, checklists, and controls
• Ongoing monitoring: Define and track key indicators such as model drift, fairness, stability, and reliability, ensuring alignment with business performance metrics and supporting implementation of alerting mechanisms
• Standards development: Help shape reusable frameworks, templates, and testing approaches across predictive, generative, and agent-based AI use cases, while promoting consistency and best practice across teams
• Continuous development: Stay informed on emerging trends and advancements in statistics, machine learning, and artificial intelligence

• Postgraduate qualification in a quantitative discipline such as Statistics, Data Science, Computer Science, Mathematics, Economics, or Engineering
• Proven experience (typically 4+ years) in model validation, governance, or risk management within regulated environments, covering both statistical and AI/ML models
• Strong understanding of modelling techniques (e.g., regression methods, survival analysis, gradient boosting, neural networks, and feature engineering practices)
• Proficient in Python and SQL
• Able to convey complex technical concepts clearly to diverse audiences and work effectively across multidisciplinary teams
• Skilled in interpreting complex documentation and translating requirements into precise and practical deliverables
• Self-driven, curious, and committed to continuous learning and improvement

• Familiarity with evaluating generative AI solutions, including prompt design and retrieval-augmented approaches
• Knowledge of model interpretability techniques, stress testing methods, and AI-related privacy and security considerations
• Understanding of reinforcement learning concepts
• Experience engaging with governance functions such as legal, compliance, or cybersecurity, and converting their requirements into actionable controls
• Awareness of common AI applications in regulated industries, along with their benefits, limitations, and potential risks

bonus