Lead Data Scientist - Pricing

We’re looking for a Lead Data Scientist to join our Pricing team in London. This is a rare chance to shape how Wise prices cross-border money transfers — using data, machine learning and experimentation to keep pushing prices down while staying sustainable. What you build will have a direct impact on Wise’s mission and the millions of customers who rely on us for fair, transparent pricing About the Role: We are seeking a skilled and detail-oriented Data Scientist to help us develop a data-driven approach to pricing strategy and execution in a highly competitive remittance market. As part of this team, you will leverage advanced analytics, machine learning, causal inference and robust experimentation to help Wise push price down, deepen our understanding of customers, and maintain a competitive edge. You will partner closely with the Pricing analytics and engineering teams, Pricing Product, FP&A, Commercial Directors and senior leadership — turning complex data into pricing decisions that move the business. Here’s how you’ll be contributing: - Pricing Experimentation Framework - Design, build and run a robust framework for testing pricing structures, fee levels and promotional offers across corridors and customer segments. - Define key metrics, significance levels and reporting; apply causal inference to isolate the true impact of price changes. - Automate reporting and insight generation so pricing hypotheses can be tested scientifically and at scale. - Price Elasticity & Revenue Modelling - Partner with the Growth team to adapt price-elasticity insights into the core repricing framework. - Forecast the impact of price changes on demand, volume and revenue, and quantify the trade-offs. - Optimise pricing for different segments based on their sensitivity, and validate predictions against experimental data. - Automation & Data Infrastructure - Build data pipelines and monitoring that make accurate, timely pricing data accessible. - Reduce manual effort in pricing analysis and monitoring through automation. - Ensure data integrity through robust validation, and share best practices across the team. - Customer Contact Analysis - Apply machine learning to classify and analyse pricing-related customer support contacts (tickets, chats, calls). - Surface common pain points and confusion (e.g. “fee too high”, “confusing fee structure”) and emerging concerns. - Provide actionable insights to Product and Operations to reduce friction and simplify fee structures.