Senior Lead Machine Learning Scientist, Fincrime

Key Skills

AWS, Azure, Deep Learning, Google BigQuery, R Programming, SQL, UI design

Job Description

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:

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.

What you’ll be working on:

As a Senior Lead, 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 advanced ML techniques that you bring to the table. 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.

More specifically, we’ll be expecting you to leverage your deep experience of developing and deploying advanced Machine Learning models 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 we’ll strongly encourage and support experimentation and innovation. We’ll 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, you’ll be providing key technical leadership and shipping highly impactful ML-based solutions. You’ll be empowered to work across the FinCrime collective identifying the most impactful areas and leading solution development.

You’ll work with our mission-oriented cross functional product squads, collaborating closely with product managers, data scientists, backend engineers and designers in an agile environment.

You’ll be expected to use your technical expertise to advise senior business stakeholders and help to set and advance our strategic direction in FinCrime.

You’ll 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.
• Leading the design and development of advanced real time Machine Learning models, for example exploring how neural network, graph-based, and sequence-based architectures can drive improvements in detection of financial crime.
• 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.

You should apply if:

What we’re doing here at Monzo excites you!

• 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
• 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

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
• 3 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 tech-hiring@monzo.com

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, 2022 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 age, ethnicity, religion, sex, sexual orientation, gender identity, family or parental status, national origin, or veteran, neurodiversity or disability status.