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  "job_title": "Senior Data Scientist, Fraud Prevention",
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  "job_description": "About Us at GoCardless\n\nGoCardless 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.\n\nGoCardless 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.\n\nWe are headquartered in the UK with offices in London and Leeds, and additional locations in Australia, France, Ireland, Latvia, Portugal and the United States.\n\nAt 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!\n\nAnd remember: we don’t expect you to meet every single requirement. If you’re excited by this role, we encourage you to apply!\n\nThe role\n\nThis role will be working within the Fraud Prevention team in our Merchant Operations Group. The Fraud Prevention team plays a critical role in protecting the integrity of the GoCardless platform by building systems that prevent and detect merchant fraud before it impacts our business or our customers.\n\nThe Fraud Prevention Data Scientist will work closely with Engineers and Fraud Analysts to develop and deploy predictive models that strengthen our fraud defenses. You’ll focus on the end-to-end delivery of ML solutions - from feature engineering and prototyping to production-grade deployment - to reduce false positives and automate controls without introducing unnecessary friction. You’ll also collaborate with cross-functional stakeholders to ensure our ML products scale on our GCP stack, driving fintech innovation while supporting a seamless customer experience.\n\nWhat you’ll do\n• Contribute to the end-to-end delivery of models at scale, from initial discovery and feature engineering to production, A/B testing and continuous monitoring.\n• Collaborate with product, engineering and data science peers to turn complex data into real-time, mission-critical fraud prevention solutions.\n• Raise the team’s collective bar through hands-on technical leadership and knowledge sharing.\n• Help bring to live the latest developments in ML and payer fraud prevention to drive innovation at GoCardless.\n\nWhat excites you\n• 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.\n• Contributing to the future of fraud prevention, by shaping up the data and ML products all the way from the initial insights to the market-ready solutions.\n• Working with a range of stakeholders to discover and design ML solutions, adapting them to the markets as we grow.\n• Building production-grade ML models on a streamlined GCP and Vertex AI stack to drive fintech innovation.\n\nWhat excites us\n• You hold a degree (or PhD) in a STEM discipline or an equivalent commercial experience.\n• You have a track record of deploying predictive models and data products in production with quantifiable impact (experience in Fintech, Fraud Prevention, or Payments is a big plus).\n• You can translate complex ML concepts into practical product solutions and communicate these ideas clearly to non-technical peers.\n• You are experienced with writing and maintaining code to a production-level standard, supporting the team with code reviews.\n• You are comfortable contributing across the full model lifecycle, from deep-dive analysis and feature engineering to prototyping, validation, and live A/B testing.\n\nBase salary range: £99,200 - £148,800\n\nBase 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.\n\nThe Good Stuff!\n• Wellbeing: Dedicated support and medical cover to keep you healthy.\n• Work Away Scheme: Work from anywhere for up to 90 days in any 12-month period.\n• Hybrid Working: Our hybrid model offers flexibility, with in-office days determined by your team.\n• Equity: All permanently employed GeeCees get equity to share in our success.\n• Parental leave: Tailored leave to support your life's great adventure.\n• Time off: Annual holiday leave based on your location, supplemented by 3 volunteer days and 4 wellness days.\n\nLife at GoCardless\n\nWe're an organisation defined by our values; We start with why before we begin any project, to ensure it’s aligned with our mission. We make it happen, working with urgency and taking personal accountability for getting things done. 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. Our Values form part of the GoCardless DNA, and are used to not only help us nurture and develop our culture, but to deliver impactful work that will help us to achieve our vision.\n\nDiversity & Inclusion\n\nWe’re building the payment network of the future, and to achieve our goal, we need a diverse team with a range of perspectives and experiences. As of July 2024, here’s where we stand:\n• 45% identify as women\n• 23% identify as Black, Asian, Mixed, or Other\n• 10% identify as LGBTQIA+\n• 9% identify as neurodiverse\n• 2% identify as disabled\n\nSustainability at GoCardless\n\nWe’re committed to reducing our environmental impact and leaving a sustainable world for future generations. As co-founders of the Tech Zero coalition, we’re working towards a climate-positive future.",
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  "job_posted_at_datetime_utc": "2026-02-26T00:00:00.000Z"
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