Lead Data Scientist

Lead Data Scientist

Hybrid - London 1 day per week

Up to £85,000

About the Role

We are working with a high-growth performance marketing and technology company who are looking for a new Lead Data Scientist to join the team.

You'll be working at the intersection of data, technology, and marketing, you’ll lead the delivery of cutting-edge machine learning, optimisation, and analytics solutions that drive measurable business outcomes.

Reporting directly to the Director of Data, you’ll take ownership of high-impact projects across areas such as customer lifetime value, predictive modelling, bidding optimisation, and experimentation, whilst also acting as a data lead for major clients.

Initially hands-on, this role will evolve into a more strategic leadership position, with team management responsibilities (starting with three Data Scientists, growing to five within a year) expected within the first six months.

Key Responsibilities

• Lead and deliver data science solutions across predictive modelling, optimisation, and experimentation.
• Act as a strategic data partner to key clients, owning technical delivery and ensuring high-quality results.
• Design and deploy machine learning models and optimisation algorithms in production environments.
• Collaborate with engineering, marketing, and analytics teams to integrate data science into campaign strategy and tooling.
• Mentor and guide junior data scientists, establishing best practices and scalable workflows.
• Help grow and shape the data science team and its role within the wider business.

What We’re Looking For

• Strong technical foundation with proficiency in Python (Pandas, NumPy, Scikit-learn), SQL, and cloud platforms (GCP or AWS).
• Experience with modern data warehouses (BigQuery, Snowflake, Redshift).
• Proven experience in deploying machine learning models or optimisation algorithms into production.
• Solid understanding of digital marketing concepts, platforms (e.g., Google Ads, Meta), and analytics tools.
• Demonstrated experience across key use cases such as:
• Customer Lifetime Value (CLV) modelling
• Propensity scoring and lead prioritisation
• Budget allocation and bidding optimisation
• Experimentation, A/B testing, causal inference, MMM
• Advanced analytics (e.g. marginal returns curves, supply-demand modelling)
• Strong communication skills with the ability to simplify complex technical concepts for stakeholders.
• Leadership potential with a willingness to coach and develop others.

Please note, that this role cannot offer sponsorship.