Data Scientist – Causal Inference (London)

About the role

The team is part of a dedicated Customer Lifetime Value (CLV) group within a global, product‑led organisation. You will apply causal inference methods to quantify the true impact of customer behaviours, product features and commercial initiatives, directly influencing growth, retention and engagement strategies.

Key responsibilities

• Apply causal inference methodologies to measure incrementality and drivers of customer lifetime value.
• Design and analyse experiments, quasi‑experiments and observational studies.
• Develop frameworks to separate correlation from causation in customer behaviour.
• Use techniques such as propensity score matching, double machine learning and counterfactual modelling.
• Measure the incremental impact of actions such as product usage or subscription adoption.
• Work with large‑scale customer and product datasets to identify value drivers.
• Collaborate with stakeholders to translate complex analysis into clear business recommendations.
• Support the productionisation of analytical solutions.

Required profile

• Strong statistical foundation with ability to apply rigorous analytical methods.
• Experience working with customer, product or subscription data.
• Academic background in a quantitative discipline (e.g., mathematics, statistics, economics).
• Proactive approach with focus on delivering commercial impact.
• Excellent collaboration and stakeholder management skills.

Required skills

• Propensity score matching
• Double machine learning
• Counterfactual modelling
• Experiment design
• A/B testing
• Statistical analysis

What we offer

• Competitive salary and comprehensive benefits package.
• Clear progression opportunities within a growing data science function.
• Exposure to cutting‑edge causal inference and experimentation methodologies.
• Opportunity to work on large‑scale, high‑impact data problems.
• Collaborative and forward‑thinking data environment.