Vice President, AI & Data Analytics

About the role

This role reports to the Head of AI & Data Analytics and builds the firm's data and AI capabilities from the ground up, working closely with Investment, Risk, Legal, Compliance and Operations teams. It is a hands-on position for someone who can turn business requirements into production-ready solutions, covering credit risk and pricing models, AI-powered document intelligence, portfolio reporting and dashboards, and natural language access to enterprise knowledge via LLMs and RAG.

Key responsibilities

• Develop and maintain predictive models for credit risk, pricing, portfolio analytics and investment decisions, including scenario analysis, stress testing and optimisation.
• Design and deploy AI applications using LLMs, RAG and AI agents, including intelligent document processing and knowledge management.
• Build scalable data pipelines, data warehouses and analytical models, with data quality, governance and monitoring frameworks.
• Integrate structured and unstructured data from internal and external sources into enterprise analytics platforms.
• Build executive dashboards and reporting solutions, define KPIs and deliver insights through visualisation tools.
• Work with stakeholders to identify analytics and AI opportunities, translate technical findings into business recommendations and support AI adoption and governance in a regulated financial services environment.

Required profile

• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Finance or a related quantitative discipline.
• 5-8 years of experience in Data Science, Machine Learning, Analytics or AI Engineering.
• At least 3 years in financial services, banking, asset management, private credit, infrastructure finance or a related industry.
• Demonstrated experience delivering production-grade machine learning or AI solutions.
• Preferred: credit risk modelling, project finance, infrastructure finance, private credit or asset-based lending; AI/ML in regulated environments; cloud platforms and modern data architecture; CFA, FRM or similar qualifications.

Required skills

• Python and SQL
• Machine learning, statistics, time series forecasting, NLP, RAG and prompt engineering
• Data warehousing, ETL, batch and streaming data processing, MLOps and CI/CD
• Data visualisation, BI, A/B testing, credit risk analytics, risk modelling and pricing optimisation