Lead Data Scientist (England)

Lead Data Scientist

Surrey office, x1 day every two weeks.

A well-established, product-led business is looking for a Lead Data Scientist to spearhead innovation and drive measurable value through advanced machine learning, experimentation, and the development of production-grade models.

Sitting within a cross-functional data team, this is a hands-on leadership role with the autonomy to shape the modelling roadmap, contribute to R&D strategy, and influence pricing and risk decisions across multiple business lines. You’ll manage a small team of data scientists, guiding them through delivery while remaining actively involved in technical implementation and experimentation.

This is a unique opportunity for someone passionate about building machine learning systems that go beyond prototypes — models that deliver real-world commercial outcomes in a data-rich, regulated environment.

Key Responsibilities

• Lead a high-performing team of data scientists to deliver cross-functional, impactful AI/ML initiatives
• Design and implement predictive models and machine learning solutions for core business areas
• Build and productionise models in collaboration with data engineers and platform teams
• Apply advanced statistical techniques to extract insights and guide product and pricing strategies
• Work closely with stakeholders to understand requirements, define modelling goals, and demonstrate business value
• Evaluate vendor data sources, assess economic and technical feasibility, and lead test-and-learn initiatives
• Contribute to the modelling roadmap, experimentation frameworks, and internal data science tooling
• Produce clean, maintainable, version-controlled code and refactor solutions into reusable libraries and APIs
• Coach junior team members and promote best practices across the wider data and analytics community

Requirements

• Ideally, 6+ years of hands-on experience applying data science techniques in commercial or research-led environments, delivering clear business outcomes
• Advanced academic background (MSc or PhD) in a technical or quantitative field such as Machine Learning, Computer Science, or Statistics
• Strong programming ability in Python (data science ecosystem) and SQL, with proven experience handling large, complex datasets
• Solid track record of building, validating, and deploying machine learning models into real-world systems
• Practical experience designing experiments, selecting evaluation metrics, and applying multivariate testing frameworks
• Leadership mindset — you’ve mentored or managed data science colleagues or helped steer technical decisions in a collaborative team
• Comfortable with version control (Git) and familiar with engineering workflows like CI/CD and containerised environments
• Skilled at working with both structured and unstructured data to unlock insights and power models
• Hands-on experience with Databricks, Apache Spark, or similar tools used in large-scale data processing
• Exposure to machine learning model deployment using APIs or lightweight serving frameworks like Flask or Keras
• Familiarity with geospatial data would be a great bonus!

If this role interests you and you would like to learn more, please apply here or contact us via niall.wharton@Xcede.com (feel free to include a CV for review).