Lead Data & AI Scientist
Hybrid: 1-2 days per week in the office (London)
Permanent
£100k-£150k DOE + Bonus + Benefits
Experis are delighted to be partnering with a leading and well-respected organisation as they continue to invest heavily in Data Science, Artificial Intelligence and Generative AI. We are supporting them in the search for an experienced Lead Data Scientist to shape, scale and lead their enterprise-wide AI capability.
This is a high-impact, strategic role with responsibility for defining the direction of Data Science and AI across the organisation. You will lead the delivery of production-grade ML and GenAI solutions, build a high-performing specialist team, and partner closely with senior stakeholders to drive measurable business value.
What You'll Be Doing
Leading the design, development and productionisation of Machine Learning, AI and GenAI solutions across the business.
Defining technical standards, best practices and governance for Data Science and MLOps.
Identifying and prioritising high-impact use cases in partnership with Wealth, Enablement and Technology teams.
Building, mentoring and developing a high-performing Data Science team.
Embedding modern MLOps practices including CI/CD, model monitoring, feature stores and version control.
Working closely with Data Engineering, Architecture and Operations teams to ensure scalable, secure deployment.
Acting as a senior technical authority and thought leader in advanced analytics and AI.
Championing innovation, experimentation and continuous improvement in AI and analytics delivery.
Experience Required
Proven experience in Head of or lead-level Data Science, AI or Machine Learning roles within enterprise environments.
Strong Python and SQL skills, with experience developing production-grade analytical solutions.
Hands-on experience with cloud platforms (Azure, AWS and/or Snowflake).
Expertise in modern machine learning frameworks (scikit-learn, XGBoost, PyTorch, TensorFlow).
Demonstrable experience delivering end-to-end ML and GenAI solutions (including RAG, LLMs, embeddings and vector databases).
Strong knowledge of MLOps tooling and practices (MLflow, CI/CD, containerisation, monitoring).
Experience leading, mentoring and developing technical teams.
If you'd like to learn more, please contact Jacob Ferdinand at