Staff Machine Learning Scientist, Financial Crime

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 it's 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.

As a Staff ML Scientist, you'll 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. We're talking about Deep Learning, Graph neural networks, transformers - you'll have space to design the architecture that will help us take our real time detection systems to the next level.

Responsibilities:

• 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.
• Work with stakeholders across the organisation to identify and scope out the most impactful opportunities to tackle Financial Crime and Fraud with Machine Learning.
• Bring 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.
• Provide technical leadership to drive up levels of technical expertise and best practice across the Machine Learning discipline, leading by example and mentoring others.
• Work 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.

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).

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.

You're 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.

You're adaptable, curious and enjoy learning new technologies and ideas.

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.

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.