Interviewing can be exciting, but it can also feel pretty opaque. You’re often trying to understand a company, a role, and a team all at the same time, while also figuring out how to show your best work.

At Monzo, we try to default to transparency. We want candidates to understand what our interview process is designed to assess, what kinds of conversations they’ll have along the way, and what success can look like in Data Science here.

Data Scientists at Monzo work on a wide range of problems across the business. Depending on the role and team, that might mean experimentation, defining metrics, shaping product strategy, uncovering opportunities and risks, building targeting models, or helping teams make better decisions with data.

We embed Data Scientists into cross-functional product squads, so the role is not just about technical analysis in isolation. It’s about working closely with product managers, engineers, designers and others to solve meaningful customer problems and drive impact.

For most individual contributor Data Science roles, the process usually looks something like this:

For more senior roles, there may also be an additional leadership-focused interview.

Throughout the process, we want to do two things: help you learn more about Monzo, and gather enough signal to work out whether this is the right role for you.

The best place to start is our careers page, where we regularly post open roles. If a particular Data Science role stands out to you, we’d generally recommend applying to that rather than applying to lots of roles at once. If we think there’s a better fit elsewhere in the discipline, we’ll talk to you about it.

You might also hear from one of our recruiters on LinkedIn. Keeping your profile up to date can make it easier for us to understand your background and reach out when something relevant opens up.

Most Data Science processes begin with a recruiter call.

This is a fairly informal conversation and usually your first chance to learn more about Monzo, the role, and how the process works. It’s also a chance for us to understand more about your background, what kind of work you’re doing today, and what you’re looking for next.

We’ll usually talk about things like:

This is also where we start to understand role fit. In Data, that can matter quite a lot. Some candidates are clearly strongest in product data science, some are more aligned to analytics engineering or machine learning, and some are still figuring that out. The recruiter call helps us work out where someone is likely to thrive.

Just as importantly, this should be useful for you too. We want you to come away with a clearer picture of Monzo, the team, and what the rest of the process will involve.

The next stage is usually an initial call with a Senior or Lead Data Scientist.

The purpose of this interview is not to test every technical skill in depth. Instead, it’s a first deeper conversation about how you’ve used data in practice, how you think about impact, and how you work with other people.

We’ll often ask you to talk through work you’ve done before - perhaps a project you’re proud of, a problem you helped solve, or a situation where you used data to influence a decision. We’re interested in the substance of what you did, but also in how you explain it.

For example, we’re looking to understand things like:

This should feel much more like a conversation than an interrogation. We know that candidates often have varied backgrounds, and not every strong Data Scientist has done the exact same work. We care more about the judgment you’ve shown, the problems you’ve tackled, and how thoughtfully you reflect on them. 

There’ll also be time for you to ask questions. We want this to be a helpful two-way discussion, and we’re always happy to talk more about how Data Science works at Monzo.

For our Data Scientist process, the technical screen has two parts:

We use this format because it lets candidates think through a problem in their own time rather than solving everything live in front of an interview panel. It also gives us a better starting point for a deeper technical discussion afterwards.

The take-home task is designed to assess a few different things at once.

First, we want to understand whether you can translate a data request into code. In practice, that usually means writing clear, sensible SQL or Python to answer business questions using data.

Second, we want to see how you approach analysis. Not just whether you can produce an answer, but whether you structure the problem well, make reasonable decisions, and focus on what matters.

Third, we want to understand how you communicate what you found. In most Data Science roles at Monzo, the work is only useful if it helps someone make a better decision. So we care about whether you can summarise results clearly and explain them in a way that works for a non-technical audience.

As with many real problems, there isn’t always a single perfect route through the task. We’ll usually give guidance on what output we’d like to see, but there is room for candidates to make choices and assumptions. That’s often part of what makes the exercise useful.

After you’ve submitted the take-home task, you’ll have a technical interview with a Data Science Manager or Lead.

Part of this conversation will focus on your take-home submission. We may ask about your logic, trade-offs you made, how you tested your work, or what you’d improve with more time. If something didn’t run as expected, that doesn’t automatically end the process - we’ll talk through it with you and try to understand whether it was a small mistake, a misunderstanding, or a more meaningful gap.

The interview will also include an analytical discussion. This is typically focused on how you would approach a product or business problem, how you think about experimentation, what success metrics you would use, and how you’d reason about evidence, trade-offs, and next steps.

Finally, there is usually a communication component. This is about explaining analytical results to a non-technical audience, drawing appropriate conclusions, and spotting when visualisations or summaries might be misleading or incomplete.

Overall, this stage is less about trivia or textbook answers, and more about whether you can think like a Data Scientist in a product environment.

The final stage usually focuses on how you would operate in the role day to day, beyond the coding and analysis itself.

For Data Science candidates, this often includes a case study interview and a behavioural interview.

Our case studies are designed to feel closer to the kinds of open-ended problems Data Scientists work on at Monzo.

You’ll be given a scenario and some context, usually linked to a realistic product or business problem. We’re interested in how you structure the problem, what questions you ask, what assumptions you challenge, and how you move from ambiguity towards a recommendation.

This is not about trying to catch you out. Interviewers may challenge parts of your thinking or push you to go deeper, but the goal is to understand how you reason through complexity, not whether you can guess the “right” answer immediately.

Depending on level, we may be looking for slightly different things here. At more junior and mid-senior levels, we’re often focused on whether you can identify sensible approaches, metrics, and trade-offs. At more senior levels, we may also look for broader judgment, prioritisation, and how you influence decisions under ambiguity.

Data Scientists at Monzo work closely with a lot of different people, so behavioural signal matters a lot.

This interview focuses on how you work: how you collaborate with stakeholders, how you navigate obstacles, how you persuade others with data, and how you leave teams or processes better than you found them.

We’re looking for examples that feel real and reflective. What was the challenge? What did you do personally? How did you handle trade-offs or disagreement? What did you learn?

These conversations matter because success in Data Science at Monzo is not just about doing good analysis. It’s also about working effectively in a cross-functional team and helping others act on what the data is telling us.

For Lead Data Scientist roles, we may include an additional interview focused on technical leadership.

Lead Data Scientists at Monzo are individual contributors, but they’re also expected to operate with broader scope and influence. That means setting a high bar technically, handling more ambiguity, leading complex work across teams, and helping others do their best work.

This interview is usually behavioural in format. We may explore topics like:

We’re trying to understand not just whether you can do strong individual work, but whether you can multiply impact through leadership, direction and judgment.

We aim to make decisions as quickly as we can, and your recruiter will keep you updated throughout the process.

When it comes to the final decision, we bring interviewers and leaders together to review the evidence from across the process. We look at the full picture rather than any one isolated answer or moment. That includes technical capability, problem solving, communication, collaboration, and role level.

We also want to make sure we’re holding ourselves to a fair and consistent standard. A good hiring decision is not just about identifying strengths - it’s also about making sure we’ve gathered the right signal, calibrated it properly, and thought carefully about where someone would be most set up to succeed.

Our goal is not just to hire great people into Data Science, but to make sure they join Monzo in a role where they can do meaningful work, grow, and have real impact. And if we think there’s a strong fit, we’ll make an offer. 

Interested in learning more about Data Science at Monzo, or exploring our open roles? Take a look at our careers page.

Team allocation in Data Science at Monzo is done based on the priority of the roles available and the preference of the candidate getting their offer. We’ll try to match you to a team that will set you up the best for success, get you to meet your prospective manager, and all being well with team fit - get an offer out to you!