The ML Model Governance Lead owns the framework, processes and tooling that ensure machine learning models deployed in production are accurate, fair, compliant and monitored — a role that is rapidly becoming mandatory in regulated industries and is emerging in all large enterprises as AI model risk becomes a board-level concern. In financial services, the SR 11-7 guidance has defined model risk management for decades. In 2026, that framework is being extended — sometimes under regulatory pressure, sometimes proactively — to cover the new generation of ML and LLM models that are increasingly making or influencing decisions that affect customers and markets. Role & Responsibilities: • Design and operate the model governance framework: model inventory, risk tiering, validation requirements, approval processes and ongoing monitoring standards • Lead model validation activities for production ML models: independent validation of model methodology, data quality, performance metrics, bias assessment and documentation completeness • Define and implement model monitoring standards: performance drift detection, data distribution shift, fairness metric tracking and automated alerting for model degradation • Own the model risk management policy and ensure alignment with regulatory requirements: SR 11-7, EBA ML guidelines, SS1/23 (UK PRA), EU AI Act model obligations • Build and maintain the model registry and documentation standards: model cards, model risk ratings, validation reports, approval records and change management documentation • Work with MLOps teams to embed governance into the model deployment pipeline: automated validation checks, staging environment requirements and production deployment gates • Manage the model governance committee: chairing review meetings, escalating high-risk models and producing governance metrics for risk committees and regulators • Build model governance tooling: integrating MLflow, Azure ML or Databricks with governance workflows, automated testing and regulatory reporting Required Skills & Experience: • 7+ years of model risk management, quantitative risk or ML engineering experience • Deep understanding of SR 11-7 or equivalent model risk management frameworks applied to ML models • Hands-on Python skills: you can read model code, run validation analyses and build monitoring scripts — not just review documentation • MLflow, Azure ML or Databricks experience for model lifecycle management • Regulatory knowledge in at least one sector: financial services (preferred), healthcare, insurance or utilities • Strong statistical knowledge: model validation methodology, bias metrics, performance measures and statistical testing • FRM, CFA, PRM or equivalent quantitative qualification is advantageous • FCA/PRA or ECB regulatory engagement experience is a strong advantage What We Offer: • Senior governance role with regulatory significance and board-level visibility • Salary £95,000–£130,000 based on experience • Hybrid working — London office with flexible remote • Direct exposure to model risk committee and regulatory engagement The ML Model Governance Lead is the professional who ensures models that make decisions about people are accurate, fair and understood. In a world where AI model failures make headlines and regulatory fines, this role matters enormously. If you have built model governance frameworks that survived regulatory scrutiny, this role is yours.
Hybrid · London | £95,000–£130,000