Vice President, AI & Data Analytics

Position Overview:

Reporting to the Head of AI & Data Analytics, this role is a unique opportunity to build the firm's data and AI capabilities from the ground up, partnering closely with Investment, Risk, Legal, Compliance, and Operations teams.

The successful candidate will design and deploy advanced analytics, AI-powered solutions, and data infrastructure to support investment decision-making, risk management, portfolio monitoring, and operational efficiency. Key initiatives include developing credit risk and pricing models, implementing AI-powered document intelligence solutions, enhancing portfolio reporting and dashboards, and enabling natural language access to enterprise knowledge through Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) technologies.

This role is highly hands-on and suited for a candidate with both strong technical expertise and the ability to translate business requirements into production-ready solutions.

Key Responsibilities:

Data Science & Advanced Analytics

• Develop and maintain predictive models for credit risk, pricing, portfolio analytics, and investment decision support.

• Design and implement machine learning, statistical, and forecasting models to address complex business challenges.

• Conduct scenario analysis, stress testing, and optimization exercises to support risk management and portfolio performance.

AI & Generative AI Solutions

• Design, develop, and deploy AI-powered applications leveraging Large Language Models (LLMs), Retrieval Augmented Generation (RAG), and AI agents.

• Build intelligent document processing and knowledge management capabilities to enable efficient access to financial, legal, and compliance information.

• Evaluate and implement emerging AI technologies to improve productivity and business outcomes.

Data Engineering & Platform Development

• Design and build scalable data pipelines, data warehouses, and analytical data models.

• Establish data quality, governance, and monitoring frameworks.

• Integrate structured and unstructured data from internal and external sources into enterprise analytics platforms.

Reporting & Business Intelligence

• Develop executive dashboards and reporting solutions to support portfolio management, investment committees, and senior leadership.

• Define key performance indicators (KPIs) and deliver actionable insights through data visualization tools.

Stakeholder Management

• Collaborate with business stakeholders to identify opportunities for analytics and AI-driven transformation.

• Translate complex technical findings into clear business recommendations.

• Support the adoption and governance of AI solutions within a regulated financial services environment.

Technical Skills:

Machine Learning, AI & Statistics

• Large Language Models (LLMs) & AI Agents

• Machine Learning & Statistical Modelling

• Natural Language Processing (NLP) & Retrieval Augmented Generation (RAG)

• Time Series Forecasting

Data Engineering & Architecture

• Python, SQL

• Data Warehousing & Data Modelling

• ETL & Data Pipelines

• Batch & Streaming Data Processing

AI Engineering & MLOps

• Prompt Engineering

• Model Deployment & Monitoring

• MLOps & CI/CD

Analytics & Visualisation

• Data Visualization & Dashboarding

• Business Intelligence

• A/B Testing & Experimentation

Risk & Financial Analytics

• Credit Risk Analytics

• Risk Modelling

• Scenario Analysis & Stress Testing

• Pricing & Optimisation

Experience & Qualifications

• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Finance, or a related quantitative discipline.

• 5-8 years of relevant experience in Data Science, Machine Learning, Analytics, or AI Engineering.

• Minimum 3 years of experience in financial services, banking, asset management, private credit, infrastructure finance, or a related industry.

• Demonstrated experience delivering production-grade machine learning or AI solutions.

• Strong programming skills in Python and SQL.

Preferred Experience

• Experience in credit risk modelling, project finance, infrastructure finance, private credit, or asset-based lending.

• Experience implementing AI or machine learning solutions within regulated environments.

• Familiarity with cloud platforms and modern data architecture.

• Professional certifications such as CFA, FRM, or equivalent quantitative qualifications are advantageous but not required.

Key Success Factors

• Strong problem-solving and analytical mindset.

• Ability to work independently in a greenfield environment.

• Excellent communication and stakeholder management skills.

• Ability to balance innovation with governance, risk, and regulatory requirements.

• Passion for leveraging data and AI to drive measurable business value.