首席机器学习科学家，金融犯罪

🚀 We’re on a mission to make money work for everyone.

We’re waving goodbye to the complicated and confusing ways of traditional banking.

After starting as a prepaid card, our product offering has grown a lot in the last 10 years in the UK. As well as personal and business bank accounts, we offer joint accounts , accounts for 16-17 year olds , a free kids account and credit cards in the UK, with more exciting things to come beyond. Our UK customers can also save , invest and combine their pensions with us.

With our hot coral cards and get-paid-early feature, combined with financial education on social media and our award winning customer service, we have a long history of creating magical moments for our customers!

We’re not about selling products - we want to solve problems and change lives through Monzo ❤️

📍London/Cardiff/UK Remote | 💰 £115,000 - £150,000 + Incentive Awards tied to your performance + Benefits ✨

About our Machine Learning FinCrime Team:

Our Financial Crime Data team consists of over 25 people across 4 data specialisms: Analytics Engineers, Data Analysts, Machine Learning Scientists and Data Scientists. As a Lead Machine Learning Scientist, you’ll be working in a fast moving environment, building and iterating on our financial crime defensive capabilities to ensure we keep Monzo and our customers safe.

Our financial crime team has a large impact on Monzo’s bottom line as fraud and scams are usually some of the largest cost line items in a bank's P&L. We have a major influence on the overall customer experience and it’s our duty to keep our customers safe. The work we do results in directly measurable customer or company benefit, which is incredibly satisfying.

Our Machine Learning Scientists work on a range of problems within the different financial crime areas ranging from fraud detection and prevention, transaction monitoring for different types of suspicious activity through to customer risk assessment and operational tooling.

What you’ll be working on:

A Lead Machine Learning Scientist at Monzo is a technical Individual Contributor (IC) leadership position. As a technical Machine Learning expert, working with billions of rows of data stored on a modern cloud native data platform, we’ll be expecting you to leverage your deep experience of developing and deploying advanced Machine Learning models to:

• Automatically and accurately detect suspicious user behaviours while minimising impact to genuine customers and operational costs

• Adapt quickly and appropriately to changing fraud and financial crime trends, ensuring our detection systems remain performant through time.

The technical approaches you take to help solve these problems will be very much in your hands and we’ll strongly encourage and support experimentation and innovation. We’ll be expecting you to justify and demonstrate effectiveness along the way, making sure the approach meets our business and customer needs.

Your day-to-day:

As a technical individual contributor, you’ll be providing technical leadership and shipping highly impactful ML-based solutions. You’ll be embedded in a cross functional product squad, working closely with product managers, data scientists, backend engineers and designers in an agile environment. You’ll also be a technical leader within the Machine Learning discipline, helping to steer technical work and drive up standards.

This will involve:

• Working with stakeholders across the organization to identify and scope out the most impactful opportunities to tackle Financial Crime and Fraud with Machine Learning.

• Leading the design and development of advanced real time Machine Learning models, for example exploring how neural network, graph-based, and sequence-based architectures can drive improvements in detection of financial crime.

• Providing technical leadership to drive up levels of technical expertise and best practice across the Machine Learning discipline, leading by example and mentoring others.

• Working closely with our MLOps team to steer the ongoing development of tools to enable rapid iteration of models and optimisations of the full ML model lifecycle.

You should apply if:

What we’re doing here at Monzo excites you!

• You have a multiple year track record of excellence leading the development and deployment of advanced Machine Learning models to tackle real business problems preferably in a fast moving tech company

• You have experience developing and shipping deep learning, graph-based, and/or sequence-based ML architectures to production and delivering business impact

• You're impact driven and excited to own the end to end journey that starts with a business problem and ends with your solution having a measurable impact in production

• You have a self-starter mindset; you proactively identify issues and opportunities and tackle them without being told to do so

• Reducing financial crime and protecting customers with data driven strategies sounds exciting to you

• You have extensive experience writing production Python code and a strong command of SQL. You are comfortable using them every day, and keen to learn Go lang which is used in many of our backend microservices

• You’re comfortable working in a team that deals with ambiguity and have experience helping your team and stakeholders resolve that ambiguity

• You want to be involved in building a product that you (and the people you know) use every day

• You have a product mindset: you care about customer outcomes and you want to make data-informed decisions

• You're excited about fast-moving developments in Machine Learning and can communicate those ideas to colleagues who are not familiar with the domain

• You’re adaptable, curious and enjoy learning new technologies and ideas

Nice to haves:

• Commercial experience writing critical production code and working with microservices

The interview process:

Our interview process involves 3 main stages. We promise not to ask you any brain teasers or trick questions!

30 minute recruiter call

45 minute call with hiring manager

Full Loop: ML Modelling Interview, Product & ML interview, Behavioural Interview, Leadership interview

Our average process takes around 3-4 weeks but we will always work around your availability. You will have the chance to speak to our recruitment team at various points during your process but if you do have any specific questions ahead of this please contact us on tech-hiring@monzo.com . Please also use that email to let us know if there's anything we can do to make your application process easier for you, because of disability, neurodiversity or any other personal reason.

What’s in it for you:

✈️ We can help you relocate to the UK

✅ We can sponsor visas

📍This role can be based in our London office, but we're open to distributed working within the UK (with ad hoc meetings in London).

⏰ We offer flexible working hours and trust you to work enough hours to do your job well, at times that suit you and your team.

📚Learning budget of £1,000 a year for training courses and conferences

➕And much more, see our full list of benefits here

If you prefer to work part-time, we'll make this happen whenever we can - whether this is to help you meet other commitments or strike a great work-life balance

#LI-REMOTE #LI-SR1 Equal opportunities for everyone

Diversity and inclusion are a priority for us and we’re making sure we have lots of support for all of our people to grow at Monzo. At Monzo, we’re embracing diversity by fostering an inclusive environment for all people to do the best work of their lives with us. This is integral to our mission of making money work for everyone. You can read more in our blog , 2026 Diversity and Inclusion Report and 2025 Gender Pay Gap Report.

We’re an equal opportunity employer. All applicants will be considered for employment without attention to age, ethnicity, religion, sex, sexual orientation, gender identity, family or parental status, national origin, or veteran, neurodiversity or disability status.

If you have a preferred name, please use it to apply. We don't need full or birth names at application stage 😊