Monzo
Lead Machine Learning Scientist, Customer Operations

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đ Weâre on a mission to make money work for everyone. Weâre 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! Weâre not about selling products - we want to solve problems and change lives through Monzo â¤ď¸
About our Machine Learning Operations team:
The challenges are significant: we aim to transform customer service by reducing the time and effort required to resolve issues, enhancing customer confidence and satisfaction. As part of Operations, youâll be at the forefront of our mission to provide unparalleled customer support experiences.
Your role will be pivotal in leveraging state-of-the-art machine learning techniques (including LLMs and autonomous agents) to understand customer problems. You will lead the development of an effective human-in-the-loop system that augments automation with the efforts of our support workforce (who we call COps). This allows us to more expediently and efficiently predict, identify, disambiguate, and route customer problems at scale to support a rapidly expanding company with global ambitions across multiple geographies.
Youâll be working closely with a team of ML engineers in Operations, embedded in product squads alongside data scientists, backend, mobile and web engineers, product managers, user researchers, designers, and operations specialists.
Your day-to-day:
As a technical individual contributor, youâll be providing technical leadership and shipping highly impactful ML-based solutions. Youâll be embedded in a cross-functional product squad, working closely with product managers, data scientists, backend engineers, and designers in an agile environment. Youâll also be a technical leader within the wider Machine Learning discipline, helping to steer technical work and drive up standards.
This will involve:
- Working with stakeholders across the organisation to identify and scope out the most impactful opportunities to transform Customer Operations with Machine Learning.
- Leading the design and development of advanced Machine Learning models, exploring how LLMs, RAG, and sequence-based architectures can drive improvements in query resolution and routing.
- Providing technical leadership to drive up levels of technical expertise and best practice across the Machine Learning discipline, leading by example and mentoring others.
- Steering ongoing technical strategy, working closely with our MLOps team and backend engineers to enable rapid iteration of models and optimisations of the full ML model lifecycle.
Reasons to use Rodeo
Iâm in my final year doing Economics and I donât know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer â it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) donât need a masters. Letâs look at the ones youâd be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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Graduate Consultant â 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
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Why you're a good match
Youâve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon â deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme â client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right â and where to focus when applying.
You should apply if:
- What weâre 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.
- 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.
- Youâre 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.
- Youâre adaptable, curious and enjoy learning new technologies and ideas.
Nice to haves:
- Experience working with customer operations, capacity planning, forecasting, or in regulated institutions.
- Commercial experience writing critical production code and working with microservices.


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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
- 60 minute ML Modelling interview
- 60 minute Product & ML interview
- 60 minute behavioural 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.
Whatâs in it for you:
- âď¸ We can help you relocate to the UK
- â We can sponsor visas
- đThis role can be based in our London office, but we're open to distributed working within the UK (with ad hoc meetings in London).
- â° 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.
- đLearning budget of ÂŁ1,000 a year for training courses and conferences
- âAnd 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 weâre making sure we have lots of support for all of our people to grow at Monzo. At Monzo, weâre 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.
Weâre 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 đ
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