Lead Data Scientist - Causal Inference

Requirements

• An ability to simplify complex systems and ambiguous problems into clear hypotheses, useful abstractions and tractable analytical questions.
• Strong analytical judgement, with the ability to get to a useful answer quickly, refine it iteratively, and know when the evidence is sufficient to support a decision.
• Strong SQL and Python skills, with experience working with large, complex and imperfect datasets.
• Strong grounding in statistical analysis (e.g., classical ANOVA, non-parametric uncertainty quantification, Bayesian estimation) and causal inference, with experience designing both controlled experiments and observational studies.
• Experience applying machine learning, statistical modelling, NLP and LLM-based techniques to analyse customer behaviour and conversational data, diagnose root causes and identify the drivers of outcomes.
• Experience designing measurement frameworks for complex customer journeys and evaluating the impact of interventions.
• Strong product and business judgement, with a track record of using Data Science to influence important business decisions and drive measurable impact.

Nice to Haves

• Experience working on customer-facing products, ideally in customer support, customer operations or another high-volume service environment.
• Experience evaluating the impact of automation or AI-powered customer experiences.
• Experience applying predictive or behavioural modelling to support product or operational decision-making.
• Experience working with sensitive customer data in environments with strong security, privacy or compliance requirements.
• Experience combining quantitative analysis with qualitative or expert insight to solve complex problems.
• Familiarity with modern analytics and machine learning tooling in a cloud environment such as AWS.

What You'll Be Doing

• Building a clear, data-driven understanding of customer support quality across human and automated channels.
• Designing metrics that reflect meaningful customer outcomes.
• Identifying root causes of issues using machine learning, statistical methods, and domain expertise.
• Turning analytical insight into proposals to improve customer experience.
• Designing and analyzing experiments to determine effectiveness of proposed changes.
• Determining the right intervention for different customer problems regarding automation or human support.

Perks and Benefits

• Not specified