💰  Borrowing is the Monzo team that runs our consumer and business lending. We call ourselves Borrowing (rather than Lending) because that's what our customers do - they borrow money from us! Keeping the customer's needs in the centre of our focus is a crucial part of Monzo's philosophy.

At Monzo, our mission is to make money work for everyone. Within Borrowing, we help to achieve this goal by constantly innovating our products to make sure they are:

🤖  Data is a horizontal discipline at Monzo that covers all aspects of the data lifecycle in 4 job families: Analytics Engineering, Data Science, Data Analytics and Machine Learning.

So Borrowing Data, the intersection of these two larger teams, is the place at Monzo where we build, curate and report all of the information we need to run the Borrowing business. It's also where we build and run the Credit Models we use to make lending decisions and measure risk, answering questions such as:

We sometimes refer to ourselves as Borrowing Data & Credit, because we do both!

We’ve made a conscious choice in this team structure to keep Data and Credit together because in Borrowing they are vital to each others’ success: Data needs credit knowledge to provide insight and add value; Credit needs fast and accurate data to build high-quality models and make good decisions 📈

At the time of writing (end of 2024) we have more than 60 folks across Borrowing Data & Credit, accounting for about one third of Monzo’s Data Discipline. We are a big team with a variety of specialisation, as well as close collaboration. As our business grows into new products and markets, we are also looking at growing our data team significantly in the coming years. 

We are hiring for a wide range of data roles across Borrowing as we continue to grow, whether setting our credit strategies, building our data pipelines, developing machine learning models for personalisation, or providing insights into our product development.  The descriptions below, written by people currently in these jobs, give you an idea of what is involved in the day to day.

Consumer credit analysts focus on the sustainable lending growth at Monzo across loans, Flex (credit card) and overdrafts. The team analyse data, forecast performance, and develop predictive models, to design best in class credit and pricing strategies for our customers. We work collaboratively with teams across engineering, marketing and product to lead initiatives boosting growth and managing risk. 

Business credit analysts and consumer credit analysts often work on similar types of projects, but the context differs. For instance, business credit analysts focus on strategies for products to limited companies or sole traders, rather than personal accounts. Like all of our roles, business credit analysts operate within a squad system, fostering collaboration and efficiency in their work.

Fin Health’s vision is to provide effective tools and support to maintain financial health and rehabilitate customers if they experience difficulties. They care about getting customers the best outcomes, making processes as easy as possible and increasing engagement. The Fin Health Data team is spread across Data Analytics, Data Science and Credit Strategy, embedded in cross-functional squads covering user experience, operations, infrastructure and credit. They run experiments, build data models, create forecasting, design reporting and discover/size the next biggest opportunities.

The CIF team looks after a wide range of regulatory models, including IFRS9, quasi-IRB, and stress-testing models, the most recent ones having been built using advanced machine learning techniques. These models are put live in our state-of-the-art implementation engines which run daily and are tracked by our industry-leading monitoring. It is the CIF team's responsibility to accurately forecast the portfolio's performance and assess how much reserve capital we might need under a variety of macro-economic scenarios, this then feeds back into the Borrowing strategy. The CIF team covers all products (consumer and business), meaning they work collaboratively with many teams across Monzo.

Borrowing Decision Scientists use machine learning and statistical models to improve the Borrowing product experience through personalised decisions. Beyond the main focus on credit risk, they are also involved in developing ML models for pricing, collection, marketing and product personalisation. They are responsible for both the development and deployment of ML models, leveraging their strong ML and statistical knowledge to innovate with new data sources and modelling methodologies. They work across all borrowing products, with an emphasis on building efficient and scalable ML frameworks and solutions, that can deliver innovative applications directly and also be leveraged by other data teams. 

Product Data Scientists help to decide what products and features should be developed, and how. They do this through strategic research and experimentation, creating a culture of data-informed decision making that gives our Product Managers and Engineers valuable insight into what our customers are looking for. Product Data Scientists use a mixture of Python, SQL and statistical know-how to conduct research and uncover these unaddressed customer needs.

Borrowing Analytics Engineers own solutions encompassing data modelling, dashboarding, and creating bespoke analytical tools. This diverse role empowers them to enhance Monzo’s Borrowing products, ultimately benefiting both the company and its customers through tailored, impactful data-driven insights.

All of these roles are posted on the Monzo careers page — you can find them under the Borrowing section. Simply click the relevant link and follow the instructions. The application consists of:

You can also find information on the package of benefits offered to Monzo employees, which include: stock options, study leave, pension and parental/adoption leave.

We hope to hear from you soon! 🚀