AI & Data Science Craft Lead

Title: AI & Data Science Craft Lead Location: Hybrid (Melrose Arch)

The AI & Data Science Craft Lead is the custodian of the AI and Data Science craft across X, bigly labs, spanning Data Science, Machine Learning, AI Engineering and MLOps/ LLMOps. The AI & Data Science Craft Lead independently leads day-to-day technical direction, craft operations and delivery assurance across tribes and domains. The role translates enterprise AI and Data Science strategy into executable technical roadmaps, sound architecture decisions, production-ready solutions and measurable business outcomes. The role provides expert oversight of advanced predictive modelling, forecasting, optimisation, experimentation, generative AI, retrieval-augmented generation, agents and enterprise integrations; establishes technical standards and quality gates; and develops talent, reusable capabilities and Centres of Excellence.

WHAT WE ARE LOOKING FOR?

Minimum

• Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering or a related quantitative field
• 10+ years of relevant experience across data science, machine learning and/or AI engineering, including delivery of production solutions
• 5+ years leading technical professionals, with experience managing teams and influencing delivery in a matrix organisation
• Proven hands-on technical leadership in at least one of advanced data science or AI engineering, with sufficient breadth to independently review, challenge and direct work across predictive ML, GenAI and production AI systems
• Demonstrated accountability for end-to-end AI/ML delivery: problem framing, modelling, experimentation, architecture, deployment, monitoring, reliability and measurable business outcomes
• Experience with generative AI, LLM applications, RAG, evaluation, guardrails and the trade-offs between prompting, retrieval and fine-tuning
• Demonstrated ability to establish technical standards, reusable patterns, communities of practice or Centres of Excellence
• Experience recruiting, coaching, performance-managing and developing technical talent, including contractors and consultants
• Independently make and defend complex architecture and engineering decisions, lead senior technical discussions and translate strategy into execution
• Practical experience with advanced predictive modelling, forecasting, optimisation, experimental design and statistical learning, and sound judgment on when simpler analytical approaches are preferable
• Experience leading technical roadmaps, production readiness reviews and cross-functional delivery of enterprise AI solutions, including cost, risk, scalability and build-versus-buy trade-offs

Advantageous

• Postgraduate qualification in Data Science, AI, Computer Science or a related field
• Experience in retail, pharmacy, healthcare, loyalty, demand forecasting and pricing
• Hands-on Databricks on AWS experience, including MLflow, Unity Catalog, Feature Store, Model Serving and Mosaic AI capabilities
• Experience with MLOps, LLMOps, model risk management, privacy, responsible AI and enterprise AI governance

WHAT YOU WILL BE DOING?

• Define and maintain standards for data science, AI engineering, ML/LLM lifecycle, experimentation, documentation, testing, version control and deployment
• Maintain role profiles, competency frameworks and technical expectations for Data Scientists, AI Engineers and ML/MLOps practitioners
• Set quality gates for data suitability, model validation, bias and robustness checks, LLM evaluation, security, deployment readiness and post-release monitoring
• Establish reproducible development and deployment practices, model/experiment tracking, registries, release controls and monitoring with platform teams
• Establish and run AI and Data Science communities of practice with charters, roadmaps and practical knowledge-sharing
• Lead architecture and engineering practices for LLM applications, RAG, AI agents, tool use, APIs, orchestration and enterprise system integration

WHO YOU ARE?

• Have deep competence in applied machine learning, statistical modelling and AI engineering, with sound judgment about when each is appropriate.
• Have a strong knowledge of LLMs, RAG, agents, model evaluation, MLOps/ LLMOps, cloud platforms and software engineering.
• Must have the ability to challenge architecture, code, experiments and model performance constructively.
• Must be an experienced leader and mentor in matrix teams; builds high standards without relying solely on reporting authority.
• Be able to communicate complex technical choices to executives and non-technical stakeholders. Influences delivery leaders, governance partners and technical teams through credibility and collaboration