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.

Nice to Haves

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.

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
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')