Staff Machine Learning Scientist, Financial Crime

As a Staff ML Scientist, youll be the most senior Individual Contributor (IC) Machine Learning Scientist across the entire FinCrime collective! This will give you a real opportunity to lead us into an exciting new phase of fraud and financial crime prevention, utilising billions of rows of data and the learnings from your previous successes in designing and building advanced Machine Learning based real time detection systems.
Were talking about Deep Learning, Graph neural networks, transformers youll have space to design the architecture that will help us take our real time detection systems to the next level.
More specifically, well be expecting you to leverage your deep experience of developing and deploying advanced Machine Learning models within the fields of financial crime, fraud, security, or trust and safety to:

Lead our ongoing journey to build an advanced, scalable, extensible, automated fraud and FinCrime detection system that effectively prevents crime while minimising impact to genuine customers and operational costs.
Ensure our detection systems can adapt quickly and appropriately to changing fraud and financial crime trends, remaining performant through time.

The technical approaches you take to solve these problems will be very much in your hands and well strongly encourage and support experimentation and innovation. Well be expecting you to justify and demonstrate effectiveness along the way, making sure the approach meets our business and customer needs.
Your day-to-day
As our most senior technical IC, youll be providing key technical leadership and shipping highly impactful ML-based solutions. Youll be empowered to work across the FinCrime collective identifying the most impactful areas and leading solution development.
Youll work with our mission-oriented cross functional product squads, collaborating closely with product managers, data scientists, backend engineers and designers in an agile environment.
Youll be expected to use your technical expertise to advise senior business stakeholders and help to set and advance our strategic direction in FinCrime.
Youll also be a technical leader within the Machine Learning discipline, helping to steer technical work and drive up standards.
This will involve:

Working with stakeholders across the organisation to identify and scope out the most impactful opportunities to tackle Financial Crime and Fraud with Machine Learning.
Bringing the learnings from your previous successes in designing and building advanced Machine Learning based real time detection systems to lead advancements in our Financial Crime and Fraud detection capabilities, for example utilising deep learning, graph-based, and sequence-based architectures.
Providing technical leadership to drive up levels of technical expertise and best practice across the Machine Learning discipline, leading by example and mentoring others.
Working closely with our MLOps team to steer the ongoing development of tools to enable rapid iteration of models and optimisations of the full ML model lifecycle., 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 tech-hiring@monzo.com. Please also use that email to let us know if there's anything we can do to make your application process easier for you, because of disability, neurodiversity or any other personal reason.
Whats 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, 2022 Diversity and Inclusion Report and 2023 Gender Pay Gap Report.
Were 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.

You have a multiple year track record of excellence leading the technical work of a team in the development and deployment of advanced Machine Learning models tackling real business problems, preferably in a fast moving tech company
You have experience developing and shipping deep learning, graph-based, and/or sequence-based ML architectures to production and delivering business impact in the domain of fraud, financial crime, security or trust and safety.
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 experience in, and a passion for, mentoring other ML practitioners, sharing knowledge and raising the technical bar across the team.
You have a self-starter mindset; you proactively identify the most impactful issues and opportunities and tackle them without being told to do so
Using advanced machine learning techniques to minimise financial crime and protect customers from fraud sounds exciting to you
You have extensive experience writing production Python code and a strong command of SQL. You are comfortable using them every day, and keen to learn Go lang which is used in many of our backend microservices
youre comfortable working in a team that deals with ambiguity and have experience helping your team and stakeholders resolve that ambiguity
you want to be involved in building a product that you (and the people you know) use every day
you have a product mindset: you care about customer outcomes and you want to make data-informed decisions
You're excited about fast-moving developments in Machine Learning and can communicate those ideas to colleagues who are not familiar with the domain
Youre adaptable, curious and enjoy learning new technologies and ideas

Were 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, tests the app and gives 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:
Our Financial Crime Data team consists of over 25 people across 4 data specialisms: Analytics Engineers, Data Analysts, Machine Learning Scientists and Data Scientists.
Our financial crime team has a huge impact on Monzo. A core value for us is protecting our users from being victims of financial crime. Stopping fraud protects our users and is one of the largest cost lines in a bank's P&L. We have a major influence on the overall customer experience and its our duty to keep our customers safe. The work we do results in directly measurable customer or company benefit, which is incredibly satisfying.
Our Machine Learning Scientists work on a range of problems within the different financial crime areas ranging from fraud detection and prevention, transaction monitoring for different types of suspicious activity through to customer risk assessment and operational tooling.
What youll be working on