Senior Data Scientist, Payment Intelligence

Gocardless; Greater London; On-site; GBP 99,000 - 149,000; Full time

Benefits offered by this job

Wellbeing support

Work away scheme

Adaptive working

Parental leave

Learning budget

Wellness days (GC Fridays)

Job summary

GoCardless is seeking a Senior Data Scientist for its Payment Intelligence team in the UK. You will own end-to-end ML model work—from concept to production—on a globally distributed payment network.

You’ll work with Python, SQL and BigQuery to deploy scalable models within a Google Cloud/Vertex AI stack. You’ll partner with Software Engineers, Product Managers and Designers to deliver real-time fraud prevention solutions, push the technical envelope, and help define the roadmap for payer fraud

Qualifications

• Degree (or PhD) in a STEM discipline or equivalent commercial experience.
• Hands-on experience with architectures such as deep learning, graph-based, or sequence-based models.
• Experience in Fintech, Fraud Prevention, or Payments is a big plus.
• Ability to translate complex ML concepts into practical product solutions and communicate to non-technical peers.
• Willingness to own the full lifecycle from deep-dip analysis and feature engineering to prototyping and live A/B testing.

Responsibilities

• Lead the end-to-end delivery of models at scale, from initial discovery and feature engineering to production, A/B testing and monitoring.
• Collaborate with product and engineering peers to turn data into real-time fraud prevention solutions.
• Raise the team’s technical bar through hands-on leadership and knowledge sharing.
• Proactively research and integrate latest ML developments to drive innovation in payer fraud prevention at GoCardless.

Skills

Deep learning

Graph-based models

Sequence models

Python

SQL

BigQuery

Fraud prevention

Fintech

Education

STEM degree or PhD

Tools

Google Cloud Platform

Vertex AI

Job description

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.

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-dip 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.

Base salary range: £99,200 - £148,800

Base salary ranges are based on role, job level, location, and market data. Please note that whilst we strive to offer competitive compensation, our approach is to pay between the minimum and the mid‑point of the pay range until performance can be assessed in role. Offers will take into account level of experience, interview assessment, budgets and parity between you and fellow employees at GoCardless doing similar work.

(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
• 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 ourvalues; 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.

Diversity & Inclusion

As of April 2025, we had 806 employees (GeeCees) globally, with 524 based in the UK, 163 based in Latvia and 119 across our other offices.

To ensure that we're representative of the world around us - and to be able to review relevant benchmarks - we ask GeeCees to voluntarily disclose diversity data. This year, the proportion of GeeCees providing data increased to 88% (up from 79% in 2024). With regards to diversity within GoCardless, we can see GeeCees identifying as:

Asian, Black, Mixed or Other — 25%

Neurodiverse — 9%

LGBTQIA+ — 9%

Disabled — 1%

Average age — 33

Female — 45%

Male — 55%

We’re rooting for you during your application and GoCardless aims to provide reasonable adjustments to make our recruitment process as remarkable and accessible as we can. Please speak to your Talent Partner if you need extra support.

If you want to learn more, you can read about our Employee Resource Groups and objectives here.

Sustainability

We’re committed to reducing our impact on the environment, leaving a more sustainable world for future generations. Check out our sustainability action plan here.

Find out more about Life at GoCardless via Twitter, Instagram and LinkedIn.

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