Grundlegend
Data Scientist
Gepostet: 18.05.2026
Abschlussdatum: 02.07.2026
Berufsreferenz: 513ad1fef55e97f5e384fb324756a19a
Stelleninformationen
Lage
Lisboa, Lisbon Metropolitan Area, Portugal
Gesellschaft
Jobio
Kunde / Arbeitgeber
Gocardless
Berufsreferenz
513ad1fef55e97f5e384fb324756a19a
Auflistungstyp
Grundlegend
EU-Arbeitserlaubnis erforderlich
Nein
Gepostet
18.05.2026
Abschlussdatum
02.07.2026
Stellenbeschreibung
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.
Fähigkeiten
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