About us
GoCardless is a global bank payment company. Over 100,000 businesses, from start-ups to household names, use GoCardless to collect and send payments through direct debit, real-time payments and open banking.
GoCardless processes US$130bn+ of payments annually, across 30+ countries; helping customers collect and send both recurring and one-off payments, without the chasing, stress or expensive fees. We use AI-powered solutions to improve payment success and reduce fraud. And, with open banking connectivity to over 2,500 banks, we help our customers make faster, more informed decisions.
We are headquartered in the UK with offices in London and Leeds, and additional locations in Australia, France, Ireland, Latvia, Portugal and the United States.
At GoCardless, we're all about supporting you! We’re committed to making our hiring process inclusive and accessible. If you need extra support or adjustments, reach out to your Talent Partner — we’re here to help!
And remember: we don’t expect you to meet every single requirement. If you’re excited by this role, we encourage you to apply!
The role
Fraud prevention is an ever-evolving puzzle, making it one of the most rewarding and impactful areas in fintech. At GoCardless, our Fraud Prevention team develops the intelligent systems that protect our platform, our merchants, and their customers.
As a Senior Data Scientist, you will guide the design and delivery of models that operate in real time across our global payment network. You’ll work at the intersection of machine learning, graph-based detection, and behavioural modelling to stay ahead of changing patterns turning research into production systems that make a tangible difference in the fight against financial crime.
You’ll collaborate closely with Engineers, Fraud Analysts, and Product Managers to help shape the technical roadmap as we expand into new markets. Our stack is centred around Google Cloud Platform and Vertex AI, using Python, SQL, and BigQuery to build and support high-performance models at scale.
What you'll do
- Contribute to the full model lifecycle, from initial discovery and feature engineering through to production, experimentation, and continuous monitoring.
- Design and refine real-time ML systems that reduce false positives and ensure a smooth, secure experience for legitimate customers.
- Foster a responsive feedback loop, adapting models as patterns evolve to ensure our defences remain robust and effective.
- Help shape the technical direction of fraud prevention ML at GoCardless, exploring new approaches as we grow globally.
- Support the team’s growth through technical mentorship, knowledge sharing, and a commitment to high-quality code.
What excites you
- Solving complex, dynamic problems: Developing systems that can adapt and respond to changing environments.
- End-to-end ownership: Seeing a project through from a conceptual business need to a live, impactful solution.
- Strategic influence: Contributing to the ML roadmap and helping determine what we build next, rather than just how we build it.
- Holistic Data Science: Engaging in deep-dive analysis, prototyping, and live experimentation.
- Modern Infrastructure: Building production-grade models on GCP and Vertex AI, with the flexibility to explore advanced architectures.
What excites us
- You hold a degree (or PhD) in a STEM discipline, or bring equivalent depth through commercial experience.
- You have hands-on experience with modelling approaches such as deep learning, graph-based methods, or sequence models. While fintech experience is great, we value transferable skills from fields like cybersecurity or risk modelling.
- You have a track record of deploying models in production that create measurable value.
- You can translate complex ML concepts into clear, practical solutions for stakeholders across the business.
- You enjoy writing clean code and helping the team thrive through thoughtful reviews and shared learning.
(some of) The good stuff
- Wellbeing - stay healthy with dedicated support and medical cover
- Work away scheme - gives you the option to work away from your country of residence for up to 90 days in any 12 month period
- Adaptive Working - allows you to work flexibly, around your lifestyle
- Equity - all permanently employed GCs get equity to help you make a valuable contribution
- Parental leave - to suit everyone embarking on life's great adventure
- Learning Budget - lead your own development with an annual learning budget
- Time off - generous holiday allowance, + 3 annual volunteer days, + 4 annual business-wide wellness days (‘GC Fridays’)
Life at GoCardless
We're an organisation defined by our values; We start with why before we begin any project, to ensure it’s aligned with our mission. We act with integrity, always. We care deeply about what we do and we know it's essential that we be humble whilst we do it. Working this way creates the GC magic- the reason we all love showing up to work.
Sustainability
We’re committed to reducing our impact on the environment, leaving a more sustainable world for future generations. In 2021 we became co-founders of the Tech Zero coalition, a group of businesses committed to taking climate action as part of the UNFCCC Race to Zero. We aim to reduce our impact and to create positive change on the natural world. Check out our sustainability action plan here.
Find out more about Life at GoCardless via Twitter, Instagram and LinkedIn.
What excites us
- You hold a degree (or PhD) in a STEM discipline, or bring equivalent depth through commercial experience.
- You have hands-on experience with modelling approaches such as deep learning, graph-based methods, or sequence models. While fintech experience is great, we value transferable skills from fields like cybersecurity or risk modelling.
- You have a track record of deploying models in production that create measurable value.
- You can translate complex ML concepts into clear, practical solutions for stakeholders across the business.
- You enjoy writing clean code and helping the team thrive through thoughtful reviews and shared learning.
