Lead AI Data Scientist

Our client is urgently looking for an experienced Lead AI Data Scientist, with good demonstrable experience with AI to join their team on an 18-month Fixed-Term basis.

Our client is looking to get AI embedded at scale – a more structured and measurable manner, into the delivery of our client’s services across the group.

This is a new role created to lead data science within that programme. Working closely with Subject Matter Experts (SMEs), business analysts and engineers, you’ll identify where within legal workflows automation/AI can deliver the greatest benefits, prototype solutions and guide them into Production.

Lead AI Data Scientist – Key Skills:

• Substantial experience (5+ years) as a data scientist, including time leading or setting direction for data science work, with a track record of taking models from prototype into Production and keeping them running.
• Experience managing, mentoring, or informally leading data scientists or analysts, including supporting hiring, setting priorities, and developing others, or a demonstrated interest in moving into people management.
• Hands‑on building and evaluating LLM‑based solutions, including prompt design, retrieval‑augmented generation, and structured evaluation approaches such as human‑in‑the‑loop review and LLM‑as‑Judge.
• Collaborating to define AI system architecture and design, along with emerging techniques such as MCP, agent skills, AI harness design and context engineering.
• Definition of success metrics and baselines for a programme or product, and designing before‑and‑after analysis to show measurable impact, ideally in terms the business cares about (productivity, quality, cost, margin).
• Monitoring models or AI‑enabled workflows in Production and identifying quality degradation or drift, with the ability to recommend and drive fixes.
• Strong data skills: assessing data quality and fitness for purpose across multiple source systems, mapping how data moves between them, and contributing to target data models. Proficiency in Python and SQL, and experience working on a cloud data platform (e.g. Azure, Databricks, Snowflake).
• Built dashboards and automated reporting for non‑technical stakeholders.
• Worked closely with engineers and architects to define paths to Production, including cost‑to‑run considerations such as consumption, licensing and cost per task.
• Ability to effectively leverage AI coding tools to accelerate prototyping, experimentation and development.
• Strong stakeholder skills: the ability to work with subject matter experts and business analysts,