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About us:

We're here to make money work for everyone and we're doing things differently. For too long, banking has been obtuse, complex and opaque.

We want to change that and build a bank with everyone, for everyone. Our amazing community suggests features, test the app and give us constant feedback so we can build something everyone loves.

We're focused on solving problems, rather than selling financial products. We want to make the world a better place and change people's lives through Monzo.

About our Machine Learning, Financial Crime Team:

As a Machine Learning Scientist, you'll be working in an ever changing environment where we are building and iterating on our financial crime defence capabilities to ensure we keep Monzo and our customers safe. As a financial crime team we have a large impact on Monzo's bottom line as fraud and scams are usually some of the largest cost line items in a bank's P&L. In addition, we have a great influence on the overall customer experience and it's our duty to keep them safe. Most of the work we do results in directly measurable customer or company benefit which is incredibly satisfying.

Our Machine Learning Scientists don't have a permanent assignment to a problem space and might rotate into different financial crime areas after some time. The major problem spaces that we are working in are: fraud prevention, transaction monitoring for different types of suspicious activity, customer risk assessment and operational tooling to name the biggest ones. At the moment we are about 21 people in financial crime data, including Analytics Engineers, Data Analysts, Machine Learning Scientist and Data Scientists.

What you'll be working on:

As part of your job you'll work closely with product managers, data scientists, backend engineers, designers and researchers in an agile product environment. You'll champion the use of data and machine learning techniques, bring ideas to life through rigorous experimentation and A/B testing. You'll help us get the most out of large volumes of data stored on a modern cloud native data platform and with spotting opportunities to make each area of financial crime work even better for our users and for Monzo.

Our culture is open and focused on collaboration between different functions and individuals. It is fast paced and innovative where we try to push the boundaries of the current financial industry best practices. We put a lot of emphasis on feedback (up, side and downwards) and personal growth and development.

Your day-to-day

You'll be a member of the machine learning discipline, working within a product squad in financial crime to help to design, build, analyse, and experiment with machine learning systems that make use of the data we gather.

We organise our machine learning projects into three distinct phases; you'll spend your time doing all three:

• Explore. We use BigQuery and Jupyter Notebooks to analyse data and design machine learning models for offline evaluation. For example, we are investigating machine learning powered assistants for our customer operations team, classifiers to detect financial fraud or to assess customer risk at signup.
• Launch. We build Python micro services and cron jobs to put promising machine learning models into production. We are actively working on automating as much of this step as possible: our goal is for any data scientist or ML engineer to be able to deploy a promising new model to production in less than a day.
• Iterate. We run A/B tests in partnership with other teams and analyse the results. Based on outcomes, we may decide to roll the model out to every customer or to explore improvements to the model for further testing.

You should apply if:
What we're doing here at Monzo excites you!

• 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
• Reducing financial crime and protecting customers with data driven strategies sounds exciting to you
• You have a solid grounding in SQL and Python, are comfortable using them every day, and keen to learn Go lang which is used in many of our microservices
• You have multiple years of experience using machine learning techniques to tackle real business problems preferably in a fast moving tech company
• You are comfortable exploring potentially ambiguous business problems within a complex and rapidly growing organisation
• You're excited about the potential of machine learning 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

Nice to haves:

• Experience from working with financial crime and regulated institutions
• You have commercial experience writing critical production code and working with microservices

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
• 1 take home task
• 2 x 1-hour video calls with various team members

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 [email protected]

What's in it for you:

✈️ We can help you relocate to the UK

✅ We can sponsor visas

This role can be based in our London office, but we're open to distributed working within the UK (with ad hoc meetings in London).

• We offer flexible working hours and trust you to work enough hours to do your job well, at times that suit you and your team.

Learning budget of £1,000 a year for books, training courses and conferences

➕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.

Equal Opportunity Statement

We are actively creating an equitable environment for every Monzonaut to thrive.

Diversity and inclusion are a priority for us and we are making sure we have lots of support for all of our people to grow at Monzo. At Monzo, embracing diversity in all of its forms and fostering an inclusive environment for all people to do the best work of their lives with us. This is integral to our mission of making money work for everyone. You can read more in our blog, 2021 Diversity and Inclusion Report and 2022 Gender Pay Gap Report.

We're an equal opportunity employer. All applicants will be considered for employment without attention to ethnicity, religion, sexual orientation, gender identity, family or parental status, national origin, veteran, neurodiversity status or disability status.

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