Senior Data Scientist, Payment Intelligence

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 (candidate motivations)

• 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 (candidate requirements)

• 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.