Lead Data Scientist - Causal Inference

We are seeking an experienced Lead Data Scientist to join our Customer Support team in London. You will help us understand how well our customer support experience is working, identify areas for improvement, and shape the path towards greater automation.

Measure and diagnose

• Build a clear, data-driven understanding of customer support quality across human and automated channels.
• Design metrics that reflect meaningful customer outcomes, including resolution quality, customer effort, process adherence, consistency, fairness, durability of resolution, and cost effectiveness.
• Identify root causes of issues by combining machine learning, statistical methods, and domain expertise.

Identify and test solutions

• Turn analytical insights into concrete proposals to improve the customer experience.
• Quantify the potential impact and value of opportunities to help the team prioritize where to invest.
• Design and analyze experiments to determine whether proposed changes genuinely improve customer and business outcomes.

Shape the automation strategy

• Determine the right intervention for different customer problems , whether they should be automated, augmented by AI, handled by people, or prevented altogether.
• Test and evaluate interventions to understand their impact on customer outcomes, operational effectiveness, and cost.

What we're looking for

• An ability to simplify complex systems and ambiguous problems into clear hypotheses and tractable analytical questions.
• Strong analytical judgment, with the ability to get to a useful answer quickly and refine it iteratively.
• Strong SQL and Python skills, with experience working with large, complex, and imperfect datasets.
• Strong grounding in statistical analysis and causal inference, with experience designing controlled experiments and observational studies.
• Experience applying machine learning, statistical modeling, NLP, and LLM-based techniques to analyze customer behavior and conversational data.

Success in this role

• Making a complex customer support system understandable enough that the team can make better decisions about it.
• Creating clear ways of thinking about customer support quality, identifying opportunities to improve outcomes, and turning insights into concrete interventions.