Senior Data Scientist, Payment Intelligence
GoCardlessLisbon, PortugalPosted 7 April 2026
Job Description
About Us at GoCardless
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
Data sits at the core of our mission. We leverage bank account data to deliver high-value, intelligent payment solutions for our customers, from enhancing payment success rates to driving payer fraud prevention.
As a Senior Data Scientist within our Payment Intelligence team, you’ll partner with Software Engineers, Product Managers, and Designers to turn big ideas into reality. You’ll own the full lifecycle of our algorithms, shaping everything from the initial concept to production-ready code that powers our global payment network.
At GoCardless, our stack is centered around Google Cloud Platform and Vertex AI, providing a high-performance environment for innovation. Our Data Scientists operate at the intersection of Python, SQL, and BigQuery to build and deploy high-performance models at scale.
What you’ll do
Lead the end-to-end delivery of models at scale, from initial discovery and feature engineering to production, A/B testing and continuous monitoring.
Collaborate with product and engineering peers to turn complex data into real-time, mission-critical fraud prevention solutions.
Raise the team’s collective bar through hands-on technical leadership and knowledge sharing.
Proactively research and integrate latest developments in ML and payer fraud prevention to drive innovation at GoCardless.
What excites you
Being a self-starter who thrives on taking a vague business problem and owning the journey from the first prototype to a live, measurable solution.
An opportunity to introduce and scale advanced architectures to solve high-stakes fraud and payment challenges.
Moving beyond execution to help define the technical roadmap for Payer Fraud Prevention, ensuring we leverage the best tools for the job.
Building production-grade ML models on a streamlined GCP and Vertex AI stack to drive fintech innovation.
What excites us
You hold a degree (or PhD) in a STEM discipline or an equivalent commercial experience.
You bring hands-on experience with sophisticated architectures, such as deep learning, graph-based, or sequence-based models (experience in Fintech, Fraud Prevention, or Payments is a big plus).
You can translate complex ML concepts into practical product solutions and communicate these ideas clearly to non-technical peers.
You are comfortable owning the full lifecycle, from deep-dive analysis and feature engineering to prototyping, validation, and live A/B testing.
You lead by example, writing clean, high-quality code and raising the team’s technical bar through knowledge sharing and code best practices.
The Good Stuff!
Wellbeing : Dedicated support and medical cover to keep you healthy.
Work Away Scheme : Work from anywhere for up to 90 days in any 12-month period.
Hybrid Working : Our hybrid model offers flexibility, with in-office days determined by you ... (truncated, view full listing at source)
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