Interviewing can feel like a lot. Even if you're excited about a role, it still takes time and energy to put yourself out there and talk through your experience.

Whether you're actively looking for a new role or simply curious about what's out there, it isn't always obvious what to expect from an interview process. We want to be transparent about how we hire for Machine Learning roles at Monzo, so you know what's coming and why we've designed things the way we have.

Our Data organisation includes Machine Learning Scientists, Data Scientists, Analytics Engineers and Data Analysts working across many areas of the business, from Financial Crime and Operations to Personal Banking and Growth.

Our interviews are designed with two things in mind:

Our Machine Learning roles follow a similar structure

For Staff level and above (typically L60+), there's an additional Project deep dive which lasts for 60 minutes

We usually start with a 30-minute conversation with one of our recruiters.

This is an opportunity for us to tell you more about Monzo, the team, and the role, while also answering any questions you have about the process. We'll talk through your experience at a high level and discuss what you're looking for next.

Most importantly, this stage helps both sides decide whether it's worth investing more time in the process.

This conversation will be with someone from our Machine Learning team.

Think of it as an opportunity to tell your story. We'll spend time understanding your background, the kinds of problems you've worked on and the impact you've had.

We'll usually ask you to talk through a recent machine learning project in some detail. We're interested in understanding the problem you were solving, the decisions you made, how you worked with others, and what you learned along the way.

You don't need to prepare slides or a formal presentation. It can be helpful, however, to think about one or two projects you've worked on recently that demonstrate both technical depth and meaningful impact.

A few things that often make for a strong discussion:

This interview focuses on how you approach machine learning problems, without the live coding. 

You'll be presented with a realistic scenario and asked to design an end-to-end machine learning solution. We're not looking for a perfect answer or a specific algorithm. Instead, we're interested in how you structure a problem, identify important considerations and make decisions when information is incomplete.

Topics that often come up include:

The best preparation is to think about how you've approached machine learning problems in the real world. Consider the trade-offs you've made, how you've measured success, and how you've iterated after launch.

Machine learning at Monzo is very close to Product.

In this interview, your interviewer will play the role of a partner from another discipline and work through a customer or business problem with you. You'll be encouraged to ask questions, explore different directions and think through possible solutions together.

We're interested in how you:

This is intended to feel collaborative rather than adversarial. The interviewer is there to provide context and answer questions as you work through the problem.

Machine learning is a team sport.

This interview focuses on experiences from your previous roles and how you've worked with others to deliver outcomes.

We'll ask about real situations you've encountered, including:

We're generally looking for specific examples rather than hypothetical answers. Thinking through a few projects beforehand can help you recall the details more easily during the conversation.

For more senior roles, we'll ask you to walk us through a single project in significant depth.

This is your opportunity to showcase a piece of work that you're particularly proud of and where you played a major role in shaping the outcome.

We're interested in understanding the full journey:

You can present this in whatever format feels most natural to you. Some candidates use slides (5 max), others prefer a document, whiteboard, or simply talking through the project.

If you're invited to this stage, we'd encourage you to choose a project where you had direct involvement and can comfortably discuss both the big-picture context and the implementation details.

Prepare a handful of detailed examples

Many of our interviews build on experiences you've had in previous roles. It can be useful to think through a few projects beforehand and refresh yourself on the context, decisions, challenges and outcomes.

Use concrete examples

Specific stories are often more helpful than general descriptions. Talk about what you personally did, what happened, and what you learned.

Think about trade-offs

Real-world machine learning rarely involves perfect information or unlimited resources. Be prepared to discuss alternatives you considered and why you chose a particular approach.

Remember it's a two-way conversation

These interviews aren't just for us to get to know you. They're also an opportunity for you to learn about Monzo, the team, and the people you might work with.

Most interviews take place over video call.

Before the interview, we recommend:

You may find benefit from structuring examples using the STAR method (Situation, Task, Action, Result).

Most importantly, come ready to tell your story and ask questions. We look forward to getting to know you.

If what you’ve read here resonates and you’re passionate about making money work for everyone, we’re hiring machine learning engineers, data analysts, backend engineers, and many more roles across Monzo! Take a look at our careers page to see if we have the right role for you.