Director of Platform, AI & Data

Reporting to the CTO with a dotted line into the CPCO this role owns the strategy and execution across Product and Engineering for three core domains: Platform AI and Data. Accountable for creating maintaining and socialising compelling prioritised roadmaps that align platform and capability supply with product demand while influencing the product roadmap with clear opportunity cost and risk signals. Responsible for adoption and internal sentiment as primary success measures ensuring we only build capabilities that teams actively use and value. Leads UK and Hyderabad (India) teams and partners balancing change and run legacy and modernisation buy/build/partner choices and security and compliance requirements.

Key stakeholders

• CTO & Technology SLT
• CPCO Product Leadership & stream-aligned product engineering teams
• Director of Strategic Insights & Analytics teams
• Senior Engineering ICs - Principal / Staff Engineers Architects
• Marketing Leadership & teams
• InfoSec Risk (2LOD) and Compliance
• Finance (budget FinOps) Procurement and Vendor partners
• UK&I BUs and global counterparts for reuse and interoperability

Core responsibilities

Product and Portfolio

• Own and socialise a rolling 4-quarter capability roadmap across Platform AI and Data; align capacity to product demand and business outcomes.
• Define and track adoption and internal sentiment targets for all capabilities; publish scorecards and retrospectives.
• Operate a transparent intake and prioritisation process balancing regulatory reliability and growth opportunities.
• Influence product roadmaps by evidencing platform leverage reuse cost-to-serve and time-to-market impacts.
• Drive clear buy vs. build vs. partner decisions with measurable value and risk trade-offs.

Platform

• Deliver internal platform capabilities that simplify and accelerate product delivery: optimisation feature toggles personalisation alerting BAU support and paved-road developer experience.
• Productise platform services and complex subsystems with SLAs/SLOs APIs documentation and clear ownership; reduce cognitive load on product teams.
• Standardise observability deployment and release practices; improve lead time change failure rate MTTR and availability.
• Promote reuse across UK&I and global BUs; retire obsolete services and reduce technical complexity.

AI

• Shape the AI domain and establish an AI-forward engineering culture with safe-by-design guardrails patterns and SDKs.
• Enable domains to build AI capabilities from within (assistive coding test generation summarisation routing risk checks) to do more with the same.
• Define model selection evaluation and monitoring practices; align with Responsible AI privacy and security standards.
• Measure AI adoption and productivity uplift; prioritise use cases with clear ROI and risk controls.
• Build out AI capabilities to power intelligent consumer experiences including on- and off-property chat integrations to enhance user engagement and service efficiency.

Data

• Execute the data strategy and evolve a best-in-class data ecosystem supporting analytics and ML.
• Embed a data-as-a-product mindset with data contracts lineage quality SLAs and clear ownership in domains.
• Advance data democratisation via self-serve tooling secure access controls documentation and enablement.
• Modernise pipelines and platforms; manage the team through change while maintaining reliability and compliance.
• Ways of working and operations
• Lead geographically distributed teams; grow Hyderabad capability and manage third parties to outcomes.
• Coach Product and Engineering leaders across your domains to drive a high-performance culture and inspire teams.
• Own engineering excellence practices and continuous improvement across SDLC and incident management.
• Partner with Risk 2LOD and Security to meet regulatory and audit requirements without slowing delivery.
• Own budget for the three domains and drive FinOps cost transparency and unit-economics improvements.
• Occasional travel to Hyderabad and partner locations.

What success looks like

• Adoption: year-on-year increase in active usage of platform capabilities by product teams; deprecation of low-value services.
• Internal sentiment: improved platform NPS/CSAT and developer experience scores across teams.
• Delivery performance: reduced lead time improved deployment frequency lower change failure rate faster MTTR.
• Roadmap health: predictable delivery against quarterly plans with clear replan rationale when needed.
• AI outcomes: measurable productivity uplift and risk-controlled AI usage across domains.
• Data outcomes: higher data product quality increased consumption and faster time-to-insight; audited lineage and access controls.
• Complexity and cost: reduced integration complexity improved reuse optimised cloud spend and unit economics.

Qualifications :

Who were looking for

Skills & competencies

• Proven leadership across Product and Engineering; excellent stakeholder management and communication.
• Portfolio and roadmap management with evidence-based prioritisation.
• Platform product management and developer experience expertise.
• Applied AI engineering and governance literacy.
• Modern data platforms data-as-product and data governance practices.
• Financial acumen vendor management and partner ecosystems.
• Change leadership in complex regulated environments.

Experiences

• Senior leadership of multi-domain teams (Platform AI Data) in distributed settings.
• Building internal platforms adopted at scale by product teams; setting SLAs/SLOs and measuring sentiment.
• Establishing AI enablement and standards; delivering AI use cases safely.
• Evolving data ecosystems and operating data-as-product; improving data quality and access.
• Balancing buy/build/partner and migrating away from legacy complexity.

Additional Information :

Internal Grade: EB6

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Remote Work :

No

Employment Type :

Full-time