About the job
About GoCardless
GoCardless is a pioneering global bank payment provider, trusted by over 100,000 businesses, from dynamic start-ups to renowned enterprises. Our platform simplifies the collection and distribution of payments through direct debit, real-time transactions, and open banking solutions.
Each year, we process over US$130 billion in payments across more than 30 countries. We empower our clients to collect both recurring and one-off payments seamlessly, eliminating stress and high fees. Leveraging AI-driven technologies, we enhance payment success and mitigate fraud risks. Our partnerships with over 2,500 banks facilitate quicker, informed decisions for our clients.
Headquartered in the UK, we have offices in London, Leeds, and additional locations across Australia, France, Ireland, Latvia, Portugal, and the United States.
At GoCardless, we prioritize support and inclusivity in our hiring process. Should you require any accommodations, please connect with your Talent Partner , we’re here to assist!
If this role excites you, we encourage you to apply, even if you don’t meet every single requirement!
The Role
Data is at the heart of our mission. We harness bank account information to create innovative, high-value payment solutions that enhance success rates and prevent payer fraud.
As a Lead Senior Data Scientist in our Payment Intelligence team, you will collaborate closely with Software Engineers, Product Managers, and Designers to transform visionary ideas into impactful realities. You will oversee the entire lifecycle of our algorithms, from initial design to production-ready code that drives our global payment network.
Our technology stack is primarily built on Google Cloud Platform and Vertex AI, providing a robust environment for cutting-edge innovation. Our Data Scientists work at the intersection of Python, SQL, and BigQuery to develop and deploy high-performance models at scale.
Your Responsibilities
- Lead the comprehensive delivery of models at scale, from initial exploration and feature engineering to production deployment, A/B testing, and ongoing performance monitoring.
