Senior Machine Learning Scientist, Customer Operations
Job description
About the role
This role centers on machine learning for customer operations inside Monzo. The hired person will own the design, implementation, and continuous improvement of data-driven solutions that directly support customer operations workflows. You will translate ambiguous operational problems into well-defined analytical challenges and build models that drive measurable improvements in customer outcomes. A core part of the role is running rigorous experiments to measure the impact of model changes and ensuring that these models remain robust and reliable in production. You will act as the technical expert for customer operations initiatives, working closely with stakeholders to understand constraints and opportunities. Success in this role requires a balance of strong technical execution and clear communication to ensure findings are actionable for non-technical partners. You will be responsible for maintaining the end-to-end lifecycle of machine learning projects, from initial hypothesis to deployed solution and ongoing monitoring.
Key facts
What you'll do
Outputs from machine learning models are developed and maintained to support customer operations workflows.
Experiments measuring the impact of changes are designed and executed to evaluate model performance.
Stakeholders in customer operations collaborate with the data team to translate operational requirements into model objectives.
Results from experiments are communicated to customer operations stakeholders to guide decisions.
Requirements
The posting states a bachelor's degree requirement. A degree in a quantitative field is required.
At least time in similar roles is required.
Experience working with stakeholders to define and solve problems using data is required.
The right to work in the UK is required for this role.
Practical notes
This role is based in Cardiff, London, or Remote within the UK.
The position is full-time with Monzo pay for this role.
The Data team owns the work for this role.
Typical interview steps
Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study.
Candidates may be asked to design a metric, interpret an experiment, or build a small model.
Some companies give a take-home analysis.
Expect questions about past projects and the business impact of your work.
Interviewers often evaluate how you communicate uncertainty and business impact, not only the math.
Bringing a clean write-up of a past analysis to the interview is well received.
Good to know
Machine learning scientists build models and experiments to solve business problems in production settings.
Collaboration with product and operations stakeholders is common in customer focused roles.
Version control and data pipeline tools are commonly used in this kind of work.
Clear communication of results to non-technical audiences is an important part of the role.
The position is full-time and eligible for UK work authorization.
Questions to ask
Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year.
Asking what past hires did well is a strong final question.
Keep the list short and pick the questions that matter most to you.
Career growth
Data careers grow toward senior analyst, staff data scientist, or data engineering lead.
Many professionals specialize in machine learning, analytics, or infrastructure.
Cross-functional work with product and engineering teams becomes more important at senior levels.
The field changes quickly, so continuous learning is part of the job.
Professionals who can translate numbers into decisions tend to advance fastest.
About the company
They'll be your partner and guide throughout the interview process.
Initial Call (1 hour) You'll meet with one of our Senior Engineering Managers or Engineering Directors.
They'll ask you about your previous experience, in particular people leadership, product delivery and technical leadership.