Data Architecture & Data Lake/Lakehouse Leadership:

- Build, lead and scale the Data Science team, defining organisation structure, roles, hiring priorities, capability roadmap and succession plans.

- Own the Data Science strategy and roadmap within the AI Track, aligned with enterprise AI priorities and business objectives.

- Lead identification, prioritisation and shaping of high-value Data Science, AI/ML, GenAI and advanced analytics use cases.

- Provide technical direction to Data Scientists; review modelling approaches, experimentation, evaluation and solution quality.

- Act as the Data Science and Data Architecture lead for the AI Track and take end-to-end ownership of designing, architecting and establishing the enterprise Data Lake / Lakehouse from scratch, while driving and coordinating the required engineering implementation.

- Assess existing enterprise data sources, systems, databases and data flows and define the target-state data architecture required for AI and advanced analytics.

- Define the Data Lake / Lakehouse architecture, including data ingestion, landing/raw, curated and consumption layers, storage, processing, orchestration, metadata and data-serving patterns.

- Define data models, domain structures, data contracts and integration patterns required to make enterprise data usable for Data Science and AI.

- Establish data ingestion and pipeline strategy for batch, near-real-time and streaming data, working with relevant engineering teams to implement the architecture.

- Define data quality, lineage, cataloguing, metadata, master/reference data, retention and lifecycle management practices.

- Define security, access control, privacy and governance requirements for the AI data platform in partnership with Information Security and enterprise architecture teams.

- Establish scalable data architecture that supports Data Science experimentation as well as production model training, inference, analytics and GenAI use cases.

- Evaluate technology choices and make architecture recommendations across data storage, processing, orchestration, analytics and AI/ML platforms, considering scalability, performance, resilience and cost.

- Establish standards and best practices for the Data Science lifecycle, model development, evaluation, documentation, governance and production readiness.

- Ensure effective conversion of Data Science ideas and POCs into scalable, production-grade solutions with measurable business outcomes.

- Build strong collaboration between Data Science, Data Engineering, AI Engineering, Architecture, Product, Infrastructure and business teams.

- Establish reusable frameworks, accelerators and reference architectures for AI and Data Science delivery.

- Promote responsible AI, model risk management, data quality, security and governance across the Data and AI lifecycle.