Wise is a global technology company, building the best way to move and manage the world’s money.
Min fees. Max ease. Full speed.
Whether people and businesses are sending money to another country, spending abroad, or making and receiving international payments, Wise is on a mission to make their lives easier and save them money.
As part of our team, you will be helping us create an entirely new network for the world's money.
For everyone, everywhere.
More about our mission and what we offer.
Job DescriptionWe 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.
You have experience with SQL and building data pipelines (dbt experience is preferable). You are confident working with complex data models and are able to independently design and implement new models.
You have strong quantitative skills. Ideally a background in statistics, mathematics, physics, engineering, analytics, computing, or any other scientific areas.
You have strong communication skills and an ability to translate business insights into persuasive analytical narratives that are simple to understand and that drives customer impact.
You have an ability to structure business problems with minimal supervision and prioritise problems collaboratively with stakeholders. You take ownership of your own impact on our customers.
Be proactive. You go beyond just the analysis and get things done.
You have experience with data visualisation tools (Looker, PowerBI, Tableau etc.) and demonstrate confident storytelling ability with data.
NICE TO HAVE BUT NOT ESSENTIAL
Experience in customer support, customer experience or operations analytics (contact demand, WFM, QA, CSAT, cost to serve)
Experience applying AI or LLMs to support or analytics workflows.
Python
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.
Keep up to date with life at Wise by following us on LinkedIn and Instagram.
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.
Keep up to date with life at Wise by following us on LinkedIn and Instagram.
Skills Required
- Experience with SQL and building data pipelines
- Ability to independently design and implement new data models
- Strong quantitative skills
- Background in statistics, mathematics, physics, engineering, analytics, computing, or another scientific field
- Strong communication skills and ability to translate insights into persuasive analytical narratives
- Ability to structure business problems with minimal supervision and prioritize collaboratively with stakeholders
- Proactive approach and ownership of customer impact
- Experience with data visualization tools such as Looker, Power BI, or Tableau
- dbt experience
- Experience in customer support, customer experience, or operations analytics
- Experience applying AI or LLMs to support or analytics workflows
- Python
Wise Compensation & Benefits Highlights
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Equity Value & Accessibility — Share-based compensation is a standard element of pay and is presented as a meaningful part of total rewards. RSU grants accompany offers in many roles, adding long-term value beyond base salary.
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Leave & Time Off Breadth — Paid time off is presented as generous in multiple locations, with US policies listing high PTO totals. Time away from work sits alongside office perks as a core strength of the overall package.
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Healthcare Strength — Health insurance is emphasized in benefits materials and is regarded as a solid component of total rewards. Medical, dental, and vision coverage are commonly included and viewed positively.
Wise Insights
What We Do
Wise is a global technology company, building the best way to move and manage the world's money. With Wise Account and Wise Business, people and businesses can hold 40 currencies, move money between countries and spend money abroad. Large companies and banks use Wise technology too; an entirely new network for the world's money. Launched in 2011, Wise is one of the world’s fastest growing, profitable tech companies. In fiscal year 2025, Wise supported around 15.6 million people and businesses, processing over $185 billion in cross-border transactions and saving customers around $2.6 billion.
Why Work With Us
We’re truly global in who we are, how we work, and how we build. Everything we do is centred around creating a world of money that’s fast, easy, fair. And open to all. Everyone who works here owns a piece of Wise, from the work they do, to the stock they hold.
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Wise Offices
Hybrid Workspace
Employees engage in a combination of remote and on-site work.
We expect new joiners in the office most days to build connections and learn from colleagues for their first six months. After that, most Wisers split their working week between the office and home, typically coming in at least 12 times a month.
