Lead Data Scientist (Classical ML, LLMs & AI Agents)

Lead Data Scientist – AI & Data Management

Company Description

Talan Data x AI is a leading Data Management and Analytics consultancy, partnering with top-tier software vendors and industry experts across various sectors. We specialise in unlocking value and deriving insights from data, with a focus on innovation through modern processes and technologies such as Machine Learning (ML) and Artificial Intelligence (AI).

Job Description

We are seeking a Lead Data Scientist to drive the technical delivery of our most high-impact AI engagements and lead a growing team of data scientists and AI engineers.

This role requires a practitioner who is equally proficient in both:

• Traditional data science (statistical modelling, machine learning, experimentation)
• Cutting-edge AI (large language models, agentic AI)

You will oversee end-to-end delivery, ensuring work is technically rigorous, commercially valuable, and aligned with strategic architecture. As a leader, you’ll:

• Mentor junior team members, raise technical standards, and foster engineering excellence.
• Act as a trusted technical advisor in client interactions, bridging the gap between possibility and practicality.
• Balance contrasting AI approaches—whether refining a forecasting model or designing an agent-driven workflow—delivering budget-conscious, high-impact solutions.

This is a technically diverse, leadership-focused role featuring varying projects (e.g., forecasting models, LLM pipelines, or agentic workflows).

Responsibilities

• Lead end-to-end AI/data science projects, ensuring technical robustness and measurable business outcomes.
• Integrate classical ML (regression, classification, forecasting, optimisation) with modern GenAI, LLMs, and agentic systems as needed.
• Mentor and upskill data scientists, AI engineers, and consultants to broaden the team’s capabilities.
• Translate business challenges into structured analytical solutions and communicate findings to non-technical stakeholders.
• Enforce best practices across the AI lifecycle—experimentation, testing, deployment, and monitoring.
• Align execution with architectural vision, ensuring technical integrity remains intact.
• Provide credible technical insight during pre-sales and client discussions, offering realistic project estimations.
• Champion reproducibility, model governance, and responsible AI in daily operations.