بنیادی

Data Scientist

Lisboa, Lisbon Metropolitan Area, Portugal کمپنی: Jobio کلائنٹ / آجر: Gocardless
پوسٹ کیا گیا: 18.05.2026
اختتامی تاریخ: 02.07.2026
ملازمت کا حوالہ: 513ad1fef55e97f5e384fb324756a19a

ملازمت کی معلومات

مقام
Lisboa, Lisbon Metropolitan Area, Portugal
کمپنی
Jobio
کلائنٹ / آجر
Gocardless
ملازمت کا حوالہ
513ad1fef55e97f5e384fb324756a19a
لسٹنگ کی قسم
بنیادی
یورپی یونین ورک پرمٹ درکار
نہیں
پوسٹ کیا گیا
18.05.2026
اختتامی تاریخ
02.07.2026

ملازمت کی تفصیل

About Us at GoCardlessGoCardless 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. If you’re excited by this role, we encourage you to apply!The roleThis 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. The 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.What you’ll doContribute to 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, engineering and data science 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.Help bring to live the latest developments in ML and payer fraud prevention to drive innovation at GoCardless.What excites youBeing a self-starter who thrives on taking a vague business problem and owning the journey from the first prototype to a live, measurable solution.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.Working with a range of stakeholders to discover and design ML solutions, adapting them to the markets as we grow.Building production-grade ML models on a streamlined GCP and Vertex AI stack to drive fintech innovation.What excites usYou hold a degree (or PhD) in a STEM discipline or an equivalent commercial experience.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).You can translate complex ML concepts into practical product solutions and communicate these ideas clearly to non-technical peers.You are experienced with writing and maintaining code to a production-level standard, supporting the team with code reviews.You are comfortable contributing across the full model lifecycle, from deep-dive analysis and feature engineering to prototyping, validation, and live A/B testing. Base salary range: €43,200 - €64,800 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.The Good Stuff!Wellbeing: Dedicated support and medical cover to keep you healthy.Work Away Scheme: Work from anywhere for up to 90 days in any 12-month period.Hybrid Working: Our hybrid model offers flexibility, with in-office days determined by your team.Equity: All permanently employed GeeCees get equity to share in our success.Parental leave: Tailored leave to support your life's great adventure.Time off: Annual holiday leave based on your location, supplemented by 3 volunteer days and 4 wellness days.Life at GoCardless We'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.Diversity & InclusionWe’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:45% identify as women 23% identify as Black, Asian, Mixed, or Other 10% identify as LGBTQIA+ 9% identify as neurodiverse 2% identify as disabled If you want to learn more, you can read about our Employee Resource Groups and objectives here as well as our latest D&I Report Sustainability at GoCardlessWe’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. Check out our sustainability action plan here. Find out more about Life at GoCardless via X, Instagram and LinkedIn.

مہارتیں

apply blended learning apply for research funding apply research ethics and scientific integrity principles in research activities build recommender systems Business Analytics Business Intelligence collect ICT data communicate with a non-scientific audience Computational Biology Computer Simulation conduct research across disciplines create data models Data Engineering data ethics Data Mining Data Models data quality assessment Data Science data visualisation software define data quality criteria deliver visual presentation of data demonstrate disciplinary expertise design database in the cloud design database scheme develop data processing applications develop professional network with researchers and scientists Digital Curation disseminate results to the scientific community draft scientific or academic papers and technical documentation empirical analysis establish data processes evaluate research activities execute analytical mathematical calculations Hadoop handle data samples Healthcare Analytics image recognition implement data quality processes increase the impact of science on policy and society information categorisation Information Extraction integrate gender dimension in research integrate ICT data interact professionally in research and professional environments interpret current data LDAP LINQ make data-driven decisions manage data manage data collection systems manage findable accessible interoperable and reusable data manage ICT data architecture manage ICT data classification manage intellectual property rights manage open publications manage personal professional development manage research data Marketing Analytics mathematical modelling MDX mentor individuals multidisciplinary research N1QL normalise data online analytical processing operate open source software perform data cleansing perform data mining perform project management perform scientific research promote open innovation in research promote the participation of citizens in scientific and research activities promote the transfer of knowledge publish academic research quantitative analysis query languages report analysis results Research Design resource description framework query language Scientific Computing scientific literature Social Network Analysis SPARQL speak different languages State Estimation statistical modeling techniques Statistics synthesise information teach in academic or vocational contexts think abstractly Unstructured Data use data processing techniques use databases use spreadsheets software visual presentation techniques write scientific publications XQuery

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