Tell us about your journey to Wise 

When I joined Wise six years ago, I was hired to lead the Customer Journey tribe—managing a team of seven with a massive mission to scale. Today, we have successfully grown that small group into a team of product analysts.

Prior to joining Wise, I spent several years at LinkedIn. Although I was always working with data, my role was initially very different to what I do at Wise. I partnered with LinkedIn’s largest business clients, leveraging the workforce data to guide them on how to scale their operations and make key expansion decisions, such as where to open new offices. This involved translating member skills, job-seeking behaviors, and macro-insights into actionable growth strategies. I transitioned to this tech environment from an academic background in mathematics, economics, and psychology.

When I decided to move from New York to London to be closer to my hometown of Istanbul, I looked for companies solving real global problems. Wise was the perfect fit. As a long-time customer who had relied on the platform for years to send money back home to Turkey, joining the team felt like a natural alignment of my personal journey and professional mission.

How does Product Analytics specifically shape the product roadmap at Wise?

At most tech companies, the relationship between Product Managers (PMs) and Product Analysts is strictly top-down. At Wise, we take it one level higher. We expect our analysts to have just as much voice and ownership over what we build—and in what order—as the PMs. It is a true, cross-functional partnership.

This collaborative decision-making happens at every level. As an analyst here, you aren't just running experiments or checking dashboards; you are driving a constant feedback loop. By projecting the potential impact of our ideas, you help us decide exactly where to focus next, testing hypotheses and using those insights to pivot.

Collaboration is vital in Product. How does your team work across different teams like Design, Engineering, Customer Support?

Every quarter, our cross-functional team comes together for a full week of collaborative planning. To define the problems we need to solve, we look at three main inputs:

• The retro: What did last quarter teach us?

• The research: What are our product, design, and support teams hearing from customers?

• The data: What do the numbers tell us about customer behaviour?

No single function dictates the roadmap. We review everything together and decide as a unified group what our next priorities should be.

What qualities make someone a great fit for the Product Analytics team? 

A standout analyst at Wise is defined by relentless curiosity and a deep sense of ownership. We look for strategic thinkers who care about how their work shapes what we build next. You need the communication skills to champion big, data-backed decisions and the agility to wear different hats.

When you look at data, you should immediately see the real-world application: What is the customer experiencing, and what do we do next? 

How are the squads using AI?

We’re changing how our analytics team works by building AI into our daily workflow. For us, this isn't about replacing people—it’s about giving our analysts their time back so they can focus on harder, more interesting problems.

We see this happening in three practical steps:

1. Cutting out the tasks that used to take days—like building complex presentations or formatting data—can now be done in hours. AI handles the heavy lifting so we can skip the manual setup
2. Ending the analyst bottleneck. We are building self-serve tools so product teams can pull routine metrics themselves. If someone just needs a quick data refresh, they won’t have to wait in an analyst's queue to get it
3. Shifting to deep discovery. By clearing out the routine requests and manual work, our analysts finally have the breathing room to dig into complex, open-ended problems—the kind of deep-dive work we simply didn’t have the bandwidth for before

Can you share a couple of examples of some of the biggest challenges the Product Analytics squads have solved?

Over the last six months, our squads have focused on turning Wise into the ultimate home for our customers' money. We looked deep into how people hold and move balances globally to figure out exactly what investment and interest-bearing features we needed to build next, changing Wise from a transactional tool into our users' primary, trusted financial hub.

Another example would be redefining what it means to travel the world seamlessly. We mapped out the entire journey of global travelers to design a one-stop ecosystem for international spending, ensuring that Wise is the only companion anyone needs when they cross a border. 

Lastly, you interview a lot for Product Analyst roles. What’s your advice for anyone looking to join Wise in Product Analytics?

When I joined Wise six years ago, I had imposter syndrome. Even though I had led larger teams in the past, the task of scaling this specific team from the ground up felt overwhelming. Over time, I realised that feeling uncomfortable is inevitable in any new role. Wise moves fast, and our methodologies are constantly evolving to match our global scale. Instead of letting the scope of the role intimidate you, look at it as an incredible opportunity to learn and expand your skill set. Focus on the value you bring, and by leaning into that exact discomfort, you will find yourself driving bigger, more impactful things than you ever thought possible.