Lead Data Scientist - Causal Inference

Salary: £100,000 - 100,000 per year

Requirements:

• Ability to simplify complex systems and ambiguous problems into clear hypotheses, useful abstractions, and tractable analytical questions.
• Strong analytical judgement, including the ability to find useful answers quickly, refine analyses iteratively, and assess when 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, such as classical ANOVA, non-parametric uncertainty quantification, or Bayesian estimation, and causal inference; experience designing controlled experiments and observational studies.
• Experience applying machine learning, statistical modelling, NLP, and LLM-based techniques to customer behaviour and conversational data to diagnose root causes and identify outcome drivers.
• 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.
• Preferred: Experience with customer-facing products, ideally in customer support, customer operations, or another high-volume service environment.
• Preferred: Experience evaluating automation or AI-powered customer experiences.
• Preferred: Experience applying predictive or behavioural modelling to product or operational decision-making.
• Preferred: Experience working with sensitive customer data in environments with strong security, privacy, or compliance requirements.
• Preferred: Experience combining quantitative analysis with qualitative or expert insight to solve complex problems.
• Preferred: Familiarity with modern analytics and machine learning tools in a cloud environment such as AWS.

Responsibilities:

• 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 by combining machine learning, statistical methods, and colleagues domain expertise to understand what drives customer outcomes.
• Turn analytical insights into concrete proposals to improve customer experience, including product changes, process improvements, automation, and better human support.
• Quantify the potential impact and value of opportunities to help our team prioritise investments.
• Design and analyse experiments to determine whether proposed changes improve customer and business outcomes; use statistical modelling and causal inference when controlled experiments are not practical.
• Determine the right intervention for different customer problems, including automation, AI augmentation, human support, or prevention.
• Test and evaluate interventions to understand their impact on customer outcomes, operational effectiveness, and cost.
• Shape our long-term automation capability roadmap and identify what we should build next to maximise value over time.

Technologies:

• AI
• AWS
• Cloud
• Support
• LLM
• Machine Learning
• Python
• SQL
• Security
• Network

More:

We are Wise, a global technology company building a better way to move and manage money worldwide, with low fees, ease, and speed. Our mission is to make international money transfers and payments easier and more affordable for people and businesses everywhere. Were looking for an experienced Data Scientist to join our Customer Support team in London, working with colleagues based in London and Budapest. This analytical, product-focused role will help us understand how well our customer support works, identify opportunities to improve it, and shape our approach to automation. Youll work closely with Product, Design, Operations, and Engineering to turn insights into action. Success means making our complex support system easier to understand and helping us make better decisions about customer outcomes, automation, human support, and future capabilities. Were committed to a diverse, equitable, and inclusive workplace, and we aim to ensure every team member feels respected, empowered to contribute, and able to progress in their career.

last updated 39 week of 2026