Lead Data Scientist - Causal Inference

We’re looking for an experienced Data Scientist to join our Customer Support team in London, working as part of a team based across London and Budapest.

You’ll help us understand how well our customer support experience is working, identify the biggest opportunities for improvement, and shape the path towards greater automation.

This is an analytical Data Science role with a strong product focus. You’ll use customer, conversational and operational data to measure service quality, diagnose issues, propose solutions and evaluate whether they improve outcomes. A key part of the role is simplifying complex customer journeys and operational systems into clear, tractable problems the team can act on.

You’ll work closely with Product, Design, Operations and Engineering to build a shared understanding of how the system works and turn insight into action.no

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 the root causes of issues by combining machine learning, statistical methods and domain expertise from colleagues to understand what genuinely drives customer outcomes.

Turn analytical insight into concrete proposals to improve the customer experience, including product changes, process improvements, automation and better human support.

Quantify the potential impact and value of opportunities to help the team prioritise where to invest.

Design and analyse experiments to determine whether proposed changes genuinely improve customer and business outcomes.

Use statistical modelling and causal inference where controlled experiments are not practical.

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.

Shape the long-term automation capability roadmap, identifying what we should build next to maximise the value delivered over time.

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.

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.

Success in this role means making a complex customer support system understandable enough that the team can make better decisions about it.

You’ll create clear ways of thinking about customer support quality, identify the biggest opportunities to improve outcomes, and turn those insights into concrete interventions that can be tested and measured.

Over time, you’ll help us make better decisions about where to automate, where to invest in human support, and which new capabilities to build — creating a support model that delivers more value to customers while becoming increasingly effective and scalable.

For everyone, everywhere. We're people building money without borders — without judgement or prejudice, too. We believe teams are strongest when they are diverse, equitable and inclusive.

We're proud to have a truly international team, and we celebrate our differences. Inclusive teams help us live our values and make sure every Wiser feels respected, empowered to contribute towards our mission and able to progress in their careers.

If you want to find out more about what it's like to work at Wise visit Wise.Jobs .

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