Data Scientist

Expectation:

• Must have strong AI Security & Compliance understanding –please reject the candidate otherwise
• Must have experience in AI Transformation & Enterprise Adoption:

Org-wide enablement track record is the differentiator, technical skills alone are insufficient; We needs someone who has scaled AI tool adoption across a large organization, built champion networks, and run measurable enablement programs

• Communication – candidate will be working directly with UK-based Engineering leaders and managers across teams and must be able to clearly articulate ML frameworks, model decisions, and technical tradeoffs

Key Responsibilities

1. Engineering, Data, Security, and IT Acceleration
   1. Own the technical depth of the Claude Code rollout in Technology: extend the Paved Paths compliance model beyond its current waves, working with the Technology Leadership team rather than in parallel to it.
   2. Partner with domain engineering leads to embed Claude into real delivery work and day-to-day engineering workflows.
   3. Work with the Data and AI team to build retrieval and agentic patterns that connect Claude to internal systems and documentation, on secure, permissioned access.
   4. Work with the Security team on tooling integration points, including Snyk and Arctic Wolf workflows, and on the security posture of any Claude-connected system.
   5. Work with Corporate IT to find and ship high-value internal automation: provisioning, ticketing, internal knowledge retrieval.
   6. Build and maintain reusable components: prompt libraries, skills, tool definitions, MCP connectors, so other teams build on a shared foundation rather than reinventing it.
2. Technology-Wide Adoption Enablement
3. Design and run a champions network that reaches every function. This is the primary vehicle for scaling adoption behaviour, not headcount.
4. Build a shared prompt and skill library open to the entire org, with a low bar to contribute and a clear ownership model so it doesn’t rot.
5. Run regular office hours and structured training, calibrated to the audience.
6. Own the intake path for “can Claude do this?” requests across the team. Triage fast and honestly. Build what belongs to this role. Route what belongs to Product, Risk, Compliance, or Legal to the right owner, with technical support rather than direct execution.
7. Track and report adoption metrics that mean something: usage depth, time saved on validated workflows, not vanity counts.

1. Governance Interface

1. Partner with Security and others on Paved Paths compliance, ISO 42001, and EU AI Act registration requirements as the programme scales.
2. Feed architecturally significant Claude integrations into the Architecture Review Board.
3. Partner with Security and Compliance on data exposure boundaries. This role does not own PCI DSS compliance for AI-assisted processes; it ensures nothing it builds or enables crosses a boundary Security hasn’t signed off on.
4. Maintain documentation and audit trails for anything this role owns, suitable for internal and external audit.

Required Qualifications

• 8+ years of overall experience, including at least 3+ years in software engineering, solutions engineering, or a technical transformation/automation role, ideally within payments, fintech, banking, or another regulated financial environment. This person needs to be credible with senior engineers, not just conversant with them.
• Hands-on experience building with LLMs in production - direct experience with the Claude API (or comparable experience with GPT/Gemini APIs and a strong willingness to specialize in Claude) including tool use, structured outputs, and context/prompt engineering.
• A demonstrated track record of scaling technical tool adoption across a large organization. This matters more than payments domain knowledge. Evidence of designing enablement programmes, champion networks, training curricula, or adoption metrics, not just shipping one tool for one team.
• Strong software engineering fundamentals: proficiency in Python and/or TypeScript/Node.js, REST/GraphQL API design, and experience integrating third-party APIs into production systems.
• Practical understanding of data security and compliance constraints relevant to financial services (PCI DSS, data minimization, access controls, audit logging).
• Excellent stakeholder communication - able to explain AI capabilities and limits clearly to both engineers and non-technical operations/risk stakeholders.

Preferred Qualifications

• Experience running an internal AI adoption or AI Center of Excellence function at scale, ideally with a visible track record similar to enablement-led (rather than build-led) transformation programmes.
• Experience with agentic workflows, RAG architectures, or MCP (Model Context Protocol) integrations.
• Familiarity with Claude Code, Claude in various IDEs, or other AI-assisted development tooling.
• Experience with evaluation frameworks for LLM output quality, safety, and reliability.
• Familiarity with commit-data-driven visibility into engineering delivery.