Specialist, Performance & Analytics
Job description
About the role
The role translates performance signals into analytical insight for the best bank in the world.
Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.
Key facts
What you'll do
Data structures turn performance signals into analytical insight that guides product and service decisions for the bank you'd be proud to use.
Metrics frameworks translate objectives into measurable outcomes so that progress for the best bank in the world can be tracked clearly.
Analysis of customer behavior informs decisions that make financial life simpler and give complete control from a smartphone.
Experiment results evaluate changes so that problems are solved based on evidence rather than selling financial products as in the past.
Cross-functional collaboration with product and engineering ensures open, collaborative, inclusive working and supports contribution to open source software.
Reporting communicates performance to stakeholders so that the community and internal teams continually engage with the organization's impact.
Requirements
A degree is required to enter this role at Monzo.
Experience analyzing performance and generating actionable insight for digital products.
Ability to work with metrics, experiments, and data structures that underpin modern banking.
Collaboration skills for open, collaborative, inclusive environments that engage with community and open source contributions.
Understanding of banking problems focused on solving customer problems rather than selling financial products.
Practical notes
The role is based in London with remote flexibility aligned to Monzo's policies.
Team context centers on performance and analytics within a product-driven engineering culture.
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
Work in this area relies on analytical rigor and clear communication.
The role interacts with product, engineering, and community teams.
Tools commonly include data platforms, experimentation frameworks, and visualization stacks.
Success depends on translating complex findings into simple, actionable guidance.
The environment values open source contribution and community engagement.
Questions to ask
Useful questions for the interview: what a typical week looks like, how work is assigned, what tools the team uses, and how feedback works. Asking how the role has changed recently and what the team wishes it had known when joining is also reasonable. Questions about the manager's priorities are especially valued.
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.