Senior Data Scientist, Financial Crime

🚀 We’re on a mission to make money work for everyone.

We’re waving goodbye to the complicated and confusing ways of traditional banking.

With our hot coral cards and get-paid-early feature, combined with financial education on social media and our award winning customer service, we have a long history of creating magical moments for our customers!

We’re not about selling products - we want to solve problems and change lives through Monzo ❤️

Hear from our team about what it's like working at Monzo ✨

About our Financial Crime Team:

We're excited to be looking for a Senior Data Scientist in Financial Crime to help build the bank of the future. You'll have the opportunity to work as a part of the Financial Crime Data Science team and play a critical role in Monzo’s mission to make money work for everyone.

What you’ll be working on

As a Data 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 Data 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 30 people in financial crime data, including Analytics Engineers, Data Analysts, Machine Learning Engineers and Data Scientists.

As part of your job you’ll work closely with product managers, engineers, designers and researchers in an agile product environment. You’ll champion the use of data, 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

• Develop and drive data solutions that improve our ability to fight financial crime and keep customers safe
• Conduct deep dives into various Fincrime systems for fraud detection, transaction monitoring and customer risk to identify key business opportunities
• Work closely with financial crime analysts, operations analysts, data scientists and engineers to understand the underlying business problem and propose an appropriate solution
• Translate regulatory reporting requirements into highly accurate data models and set the strategy for how we ensure the best possible data accuracy
• Build robust data models, reports and visualisations downstream of backend services (mostly in BigQuery SQL) that support internal management information as well as governance and regulatory reporting
• Investigate and effectively work with colleagues from other disciplines to address and improve data quality

You should apply if:

• Reducing financial crime and protecting customers with data driven strategies sounds fun to you
• You have multiple years of analytics experience, preferably in a fast moving tech company or consultancy
• You are comfortable exploring potentially ambiguous business problems and enjoy finding technical solutions to them
• You’re as comfortable getting hands-on as well as taking a step back and thinking strategically and proactively identifying opportunities
• You have experience working together and collaborating with senior business stakeholders and product teams
• You're familiar with using a variety of Data Science tools (from business intelligence, experimentation and causal inference through to machine learning), and coding languages (SQL and Python). You know when to pick the right tool, and can help others do the same

The Interview Process:

Our interview process involves 4 main stages:

• Recruiter Call (30 mins)
• Initial Call (45 mins)
• Take Home Task (to complete at home)
• Final stage (x2 interviews of 60 mins)

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

#LI-NB2 #LI-Remote

Equal opportunities for everyone

Diversity and inclusion are a priority for us and we’re making sure we have lots of support for all of our people to grow at Monzo. At Monzo, we’re embracing diversity by 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, 2022 Diversity and Inclusion Report and 2023 Gender Pay Gap Report.

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