Can you use causal inference to distinguish genuine impact from correlation?
Have you applied propensity methods or counterfactual modelling to customer or product data?
Would you like to help move an established Data Science team from prediction into causality?
A major global consumer technology business is hiring a Data Scientist into its Customer Lifetime Value team.
The team already has predictive models in place. The next step is moving beyond prediction to understand causality: what is the true incremental value of a customer action, and what would have happened if that action had not occurred?
Responsibilities
• Build causal solutions to quantify the incremental value of customer behaviours
• Apply propensity scoring, propensity score matching, Double Machine Learning and counterfactual methods
• Move existing customer value capabilities from prediction towards causality and incrementality
• Build and productionise analytical solutions using an exceptionally rich behavioural dataset
• Translate complex statistical findings into clear product and commercial outcomes
Requirements
• Strong statistical foundations and genuine depth in causal inference methods
• Experience with propensity methods, Double Machine Learning, counterfactuals or related techniques
• Experience working with customer or product data, ideally in a subscription-based business
• Ability to build and productionise analytical solutions, rather than purely conduct research
• Strong communication skills and a quantitative STEM Master’s degree preferred
Key Details
Level: Data Scientist (Mid-to-Senior level)
Salary: Up to £100,000 base + bonus
Location: London
Working model: Largely remote, visits to Central London office once every six weeks
Interested? Apply below or get in touch for more information.
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