AI Specialist, Group Data & Analytics, Group Strategy & Innovation

Responsibilities:

• Assess and qualify AI opportunities, validating business needs, technical feasibility, solution design, data sources, integrations, and delivery approach before development investment is committed.
• Lead the design, development, and iteration of AI solutions and proofs of concept, ensuring the simplest viable approach is adopted and supporting transition to production.
• Provide technical advisory and governance for AI initiatives across business units, reviewing solution designs, model selection, architectures, and adherence to enterprise standards and guardrails.
• Define, implement, and monitor AI evaluation frameworks, including performance metrics, benchmarking, validation criteria, and ongoing assessment for drift, degradation, bias, and overall effectiveness.
• Develop and maintain reusable AI assets and enterprise standards, including pipelines, evaluation frameworks, reference architectures, prompt patterns, and technical guidance to accelerate delivery and reduce duplication.
• Contribute to enterprise AI strategy and roadmaps, recommending appropriate delivery models, reusable components, effort estimates, and supporting governance decision-making through evidence-based recommendations.
• Act as an AI subject matter expert and enabler, delivering training, awareness initiatives, technical guidance, and coaching to support safe, scalable, and responsible AI adoption across the organisation.

Requirement:

• Degree in Computer Science, Data Science, Statistics, Engineering, Mathematics or a related quantitative discipline.
• Solid hands-on experience delivering data science, machine learning or generative AI solutions
• Strong practical command of Python and SQL, modern ML and LLM frameworks, prompt and agent design, retrieval-augmented generation, and cloud-based AI platforms.
• Ability to translate ambiguous business demand into a scoped, testable solution, and to communicate clearly with both senior stakeholders and non-specialist users.
• Experience in banking, financial services or another regulated industry, and familiarity with model risk, data privacy and AI governance expectations.
• Exposure to MLOps and LLMOps practice: versioning, deployment, monitoring, drift detection and cost management of live AI services.