Lead Machine Learning Scientist, Search

About the role

This role shapes the intelligent core of the Monzo Co-pilot, a strategic effort to make money work for everyone by turning banking into an intuitive, proactive assistant. The team delivers models that guide customers through financial decisions with clarity and speed.

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

Design and deploy advanced ML models that power search and discovery for more than 15 million customers, making it effortless to find the right financial answers.

Own the end-to-end journey from ambiguous business problems to measurable production impact, justifying and demonstrating effectiveness.

Guide experimentation and innovation, selecting technical approaches that solve customer problems while supporting fast iteration on our cloud-native platform.

Requirements

Demonstrate a multiple year track record of leading the development and deployment of advanced ML models to solve real business problems, preferably in a fast-moving tech company.

Show a history of shipping ML models to production and delivering clear business impact.

Work comfortably within an ambiguous environment and help teammates resolve uncertainty while maintaining customer-first outcomes.

Speak Python fluently and have hands-on experience with scikit-learn, comfort with SQL, and a willingness to learn Go for backend microservices.

Adopt a product mindset focused on customer outcomes and data-informed decisions.

Communicate complex ML concepts to colleagues without specialized domain knowledge.

Be adaptable, curious, and eager to learn new technologies and ideas.

Nice to have

Experience with personalization, ranking, and recommendation systems for consumer applications.

Commercial background writing critical production code and working with microservices.

Practical notes

Flexible working hours are offered alongside a learning budget and visa sponsorship.

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 models power search, recommendation, and decision support in consumer fintech.

Embeddings, bandits, and ranking algorithms are common techniques for personalization and real-time optimization.

Explainability and responsible AI practices are important for trust and transparency in financial services.

Fast-moving product teams iterate quickly and expect measurable impact from data-driven solutions.

Clear communication across technical and non-technical stakeholders is essential in product-focused ML work.

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.