Join a dedicated team responsible for the independent review, analysis and validation of AI/GenAI models, empowering the Bank to embrace innovative technology while efficiently handling associated risks.
Assist with independent validation and periodic reviews of AI and GenAI models, ensuring models are robust, fair, explainable, and aligned with applicable regulations.
Engage with a diverse array of use cases, improving your technical expertise across the LBG modelling landscape and throughout the entire model lifecycle, from development to monitoring and maintenance.
Develop benchmark models and conduct quantitative analysis / recoding, predominantly using Python/GCP.
Review model documentation to evaluate the soundness of the model and infrastructure, from inputs to use of the outputs.
Raise validation challenges/findings owners and follow through to resolution.
Provide clear, concise, and actionable reporting on validation outcomes and AI risk assessments to senior members of the team.
Applying judgement in reviewing and supporting Risk Classifications to enable rapid assessment of model risk.
Contribute to the improvement of validation frameworks and processes related to AI models.
Track emerging trends in AI technologies, regulatory requirements, and industry standards, to ensure our practices are aligned with the evolving AI landscape.

Professional experience working in AI Model Development / Validation, or a similar quantitative role within financial services or other regulated industries; or a recent relevant PhD.
A numerate degree or equivalent experience, for example in Data Science, Statistics, Mathematics, Computer Science or Physics.
Strong analytical and problem-solving skills with the ability to critically evaluate complex AI systems and models.
Excellent written and verbal communication skills, with an ability to communicate complex quantitative concepts clearly to non-technical customers.
Ability to work proactively and independently, manage your time optimally, and deliver high-quality outputs within set timelines.
Proficiency in Python programming and experience using AI-specific frameworks or libraries such as PyTorch, TensorFlow, LangChain, LlamaIndex, or similar tools; proficiency in SQL.
Hands-on experience or strong theoretical knowledge of AI and GenAI techniques and methodologies.
Familiarity with cloud AI platforms such as GCP Vertex AI and BigQuery, Azure AI, or similar enterprise-level AI deployment environments.
Experience or knowledge of regulatory requirements and frameworks relevant to AI, such as the EU AI Act, GDPR, SS1/23, and industry best practice in AI ethics and governance.

Assistant Manager, AI Independent Validation – Model Risk Office

Key skills