The Opportunity
A fast-scaling international fintech payments business is entering a major phase of AI-led transformation and data maturity. Operating across the UK and Europe, the organisation delivers embedded finance, payment processing, digital wallet infrastructure, and API-driven financial services to a broad portfolio of enterprise and high-growth clients.
As the business continues to scale, AI and data are becoming central to both operational efficiency and long-term commercial strategy. The company is now seeking its first Head of AI & Data to build and lead a modern AI and data capability that supports product innovation, automation, fraud prevention, customer intelligence, and strategic decision-making.
This is a critical leadership role focused on moving the organisation from fragmented data and early AI experimentation into a scalable, commercially driven AI and data function.
The successful candidate will combine strong technical capability with strategic leadership and commercial awareness, helping shape how AI and data become embedded across the wider business.
This is a role for a builder, someone capable of creating the foundations, operating model, team structure, and roadmap required to scale AI and data capability across a regulated fintech environment.
Responsibilities
1. Build the AI & Data Strategy
- Define the long-term AI and data vision aligned to company growth objectives.
- Establish a scalable roadmap covering data infrastructure, AI adoption, governance, and analytics maturity.
- Identify high-value AI opportunities across operations, customer experience, fraud, risk, and revenue generation.
- Create clear prioritisation around AI investment and delivery.
2. Develop AI Capability Across the Business
- Lead the deployment of AI-powered products, workflows, and automation initiatives.
- Introduce scalable machine learning and generative AI capability.
- Oversee the development of LLM-powered applications and intelligent internal tooling.
- Drive adoption of AI across operational and customer-facing functions.
3. Establish Modern Data Infrastructure
- Build a scalable cloud-based data platform capable of supporting analytics and AI workloads.
- Improve data quality, accessibility, governance, and reporting capability.
- Establish robust data engineering and architecture standards.
- Create a unified approach to data ownership and lifecycle management.
4. Lead AI Governance & Compliance
- Ensure AI systems operate within regulatory and governance frameworks.
- Establish responsible AI standards around explainability, bias, security, and auditability.
- Partner closely with Risk, Compliance, Security, and Legal teams.
- Prepare the organisation for evolving AI governance requirements.
5. Build & Lead the Team
- Build a high-performing AI and data function across engineering, analytics, and machine learning.
- Define hiring plans, operating structure, and capability requirements.
- Coach and mentor technical talent across AI, data engineering, and analytics.
- Establish a delivery-focused, commercially aligned culture.
6. Partner Cross-Functionally to Drive Growth
- Work closely with Product, Technology, Operations, Compliance, and Commercial leadership.
- Ensure AI and data capability directly supports business objectives.
- Translate technical opportunities into measurable commercial outcomes.
- Act as a strategic advisor to senior leadership on AI and data opportunities.
Key Responsibilities
- AI & Machine Learning Leadership: Define and lead enterprise AI initiatives, oversee ML model deployment and operationalisation, drive adoption of generative AI and intelligent automation, establish scalable MLOps capability.
- Data Strategy & Engineering: Own the organisation’s data architecture and platform strategy, improve reporting, business intelligence, and operational insights, build scalable data pipelines and governance frameworks, lead modernisation of data tooling and infrastructure.
- Governance, Risk & Security: Establish AI governance policies and controls, ensure compliance with financial services and data regulations, partner with Information Security and Risk teams on AI-related risks, improve transparency and explainability across AI systems.
- Commercial & Product Enablement: Support AI integration into customer products and internal workflows, partner with Product teams on AI-powered features and services, identify opportunities to improve operational efficiency and revenue growth through AI.
- Leadership & Stakeholder Management: Lead internal AI and data transformation initiatives, present strategy, progress, and capability plans to senior leadership and board stakeholders, build alignment between technical and non-technical teams.
Requirements
The ideal candidate will have:
Essential
- Experience leading AI, machine learning, and data functions within: Fintech, Payments, SaaS, Financial services, Technology businesses.
- Strong understanding of: AI/ML systems, Generative AI, LLM orchestration, Data engineering, Cloud infrastructure, MLOps.
- Experience building scalable data and AI platforms.
- Strong commercial understanding of how AI drives operational and revenue outcomes.
- Experience leading technical teams through growth and transformation.
- Ability to communicate effectively with executive stakeholders.
Desirable
- Experience in regulated financial environments.
- Exposure to fraud detection, payments intelligence, or risk analytics.
- Experience implementing AI governance frameworks.
- Experience scaling teams in high-growth or transformation-focused businesses.
Technical Environment
Exposure to some or all of the following is beneficial:
- Python
- PyTorch / TensorFlow
- SQL
- Vector databases
- LangChain / LlamaIndex
- MLflow
- Airflow
- Kubernetes
- Docker
- AWS / Azure / GCP
- Snowflake / Databricks
- CI/CD tooling
- Data warehousing technologies
Leadership & Style
Requires a leader who is:
- Strategic but highly pragmatic.
- Commercially aware and outcome-focused.
- Comfortable operating in ambiguity and scale-up environments.
- Hands-on enough to guide technical direction when required.
- Collaborative across Product, Technology, Operations, and Compliance.
- Calm and structured under pressure.