Lead Data Analyst, Customer Support

We are looking for a Lead Data Analyst who is passionate about our mission of Money Without Borders. You will partner with our Customer Support organisation to help drive data-driven decisions that improve customer outcomes while managing cost to serve and team capacity. Customer Support is the largest customer-facing area in Servicing. It is where cost to serve, AI and automation, and the alignment between Operations and Product come together. You will measure whether changes to channels, self-serve flows and AI agents are working for customers, and you will be a trusted analytical voice for CS Operations, CS Product and WFM.

As a Lead Data Analyst, you will own key parts of the customer support metric tree, measure the impact of automation and AI, and run experiments and causal analyses that show what really changed. You will also help make sure the team's metrics are defined once, owned clearly and trusted by everyone who uses them. This is a great opportunity for an analyst who has a keen understanding of customer problems, strong product thinking and is passionate about creating convincing analyses. You will also be part of the Servicing Analytics community of 86 analysts and a wider community of 150+ analysts at Wise, where you can collaborate on cross-team projects, share knowledge and help improve how analytics works across the company.

This is an IC3 role. For more information on our Analytics Career Map and levelling structure, click here. WHAT YOU'LL BE DOING

• Collaborate with senior stakeholders across Customer Support and effectively communicate your insights into real change for our customers. Join us in working towards our mission.
• Own key areas of the CS metric tree, for example contact rate, first response, resolution, CSAT, repeat contact and cost per contact, and make sure they roll up cleanly into Servicing's Cost and Convenience toplines.
• Support target-setting and tracking with Ops and Product leaders, so the team can see whether targets are being hit, not just set them.
• Measure AI agent, self-serve and deflection impact. Estimate addressable volume, actual deflection and cost saved, and the effect on CSAT, repeat contact and quality.
• Monitor automation quality. Support calibration and drift monitoring on quality scoring so automated decisions stay trustworthy.
• Support capacity planning and forecasting with WFM, including demand forecasts, staffing models, SLA and wait-time trade-offs, and regional and outsourcing mix.
• Make trade-offs explicit. Show the impact on customer outcomes, operational cost and team capacity in every channel and staffing recommendation.
• Build and maintain high-quality data models and pipelines in SQL and dbt, and review analytical work from colleagues to keep the bar high.