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

About our Machine Learning Business Banking team:

Our mission in Business Banking is to simplify banking for small businesses; making business banking fairer, simpler and more transparent. Monzo Business Banking is fast-growing, is the UK’s most recommended business account for overall service quality. We recently hit 1 million customers and are used by 1 in 6 UK SMEs.

We want to help businesses spend less time on financial admin and more time focused on running and growing their business. We’re building intelligent, data-driven and AI/ML-enabled product experiences that help small businesses feel more in control, make better decisions, and get more value from Monzo.

ML is at the core of how we build and scale our products, enabling Monzo to make better decisions, faster, and helping us serve businesses more effectively.

As a Lead Machine Learning Scientist in Business Banking, you’ll help build and ship models that power the next generation of Business Banking experiences. You’ll work closely with Product, Engineering, Design, Research, Data Science and Analytics Engineering to bring ML-enabled product capability into the heart of how we build for businesses.

Whether it’s helping businesses understand their cash flow, reducing manual admin, surfacing the right insight at the right moment, or helping teams build intelligent product experiences responsibly, our work makes business banking smarter, simpler and more useful for SMEs.

What you'll be working on

As a Lead Machine Learning Scientist, you’ll be a technical leader and hands-on individual contributor, spearheading our ML and GenAI capabilities and shipping key models that powers magical Business Banking experiences.

You’ll:

• Be one of the first ML Scientists in Business Banking, bringing leadership through ambiguity by adding structure and direction to our ML capabilities while staying agile and building momentum.
• Develop and deploy advanced ML models on our cloud-native data platform to serve hundreds of thousands of business customers.
• Partner with Product, data science and other stakeholders to identify the highest-impact opportunities, size impact, and define success metrics.
• Decide when to use ML, GenAI, or simpler approaches and be clear on trade-offs.
• Design robust evaluation and monitoring so we can ship responsibly and measure impact in production.
• Lead the design and implementation of batch and near-real-time models (e.g. LLM-powered experiences, predictive models, time-series forecasting).
• Collaborate closely with MLOps and Backend Engineering to operationalise models end-to-end and raise the bar on ML lifecycle rigor.
• Set technical standards, mentor others, and support experimentation and iteration.

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 state-of-the-art ML models 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 speak Python fluently and have extensive, hands-on experience with scikit-learn. You are comfortable using SQL, and keen to learn Go, 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 small businesses use to run and manage their financial lives 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 AI 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.
• You’re excited by the opportunity to help Business Banking evolve into AI and ML-enabled product capability.
• You can work closely with Product, Engineering, Design, Research and Data colleagues to shape product strategy, clarify trade-offs and build things that customers actually use.
• You’re thoughtful about responsible ML/AI; especially in financial products where trust, transparency and customer control matter.

Nice to haves:

• Experience working on personalisation, ranking, recommendation, forecasting, classification or decisioning problems for customer-facing applications.
• Experience working on ML systems for fintech, banking, accounting, payments, lending, invoicing, cash flow, tax, risk or business software.
• Experience with GenAI, LLMs, agentic workflows, retrieval, evaluation frameworks, or human-in-the-loop product experiences.
• Commercial experience writing critical production code and working with microservices.
• Experience designing experiments, A/B tests or other approaches to measure product and customer impact.
• Experience working in regulated environments or with products where explainability, auditability and customer trust are especially important.

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

What’s in it for you:

➕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

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