At a Glance
- Tasks: Develop and deploy predictive models to prevent fraud and protect our platform.
- Company: Join GoCardless, a global leader in bank payments with a commitment to innovation.
- Benefits: Enjoy flexible working, equity options, wellness days, and dedicated support for your health.
- Why this job: Make a real impact in fintech by shaping the future of fraud prevention.
- Qualifications: Degree in STEM or equivalent experience; strong skills in deploying predictive models.
- Other info: Be part of a diverse team that values integrity, accountability, and sustainability.
The predicted salary is between 36000 - 60000 ÂŁ per year.
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 AIâpowered 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.
At GoCardless, weâre all about supporting you! Weâre committed to making our hiring process inclusive and accessible. If you need extra support or adjustments, reach out to your Talent Partner â weâre here to help! And remember: we donât expect you to meet every single requirement. If youâre excited by this role, we encourage you to apply!
This 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. Youâll focus on the endâtoâend delivery of ML solutions â from feature engineering and prototyping to productionâgrade deployment â to reduce false positives and automate controls without introducing unnecessary friction. Youâll also collaborate with crossâfunctional stakeholders to ensure our ML products scale on our GCP stack, driving fintech innovation while supporting a seamless customer experience.
What youâll do:
- Contribute to the endâtoâend 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 realâtime, missionâcritical fraud prevention solutions.
- Raise the teamâs collective bar through handsâon 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 you:
- Being a selfâstarter 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 marketâready solutions.
- Working with a range of stakeholders to discover and design ML solutions, adapting them to the markets as we grow.
- Building productionâgrade 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 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 nonâtechnical peers.
- You are experienced with writing and maintaining code to a productionâlevel standard, supporting the team with code reviews.
- You are comfortable contributing across the full model lifecycle, from deepâdive analysis and feature engineering to prototyping, validation, and live A/B testing.
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 12âmonth period.
- Hybrid Working: Our hybrid model offers flexibility, with inâoffice days determined by your team.
- Equity: All permanently employed GeeCees get equity to share in our success.
- Parental leave: Tailored leave to support your lifeâs great adventure.
- Time off: Annual holiday leave based on your location, supplemented by 3 volunteer days and 4 wellness days.
Life at GoCardless:
Weâre an organisation defined by our values; We start with why before we begin any project, to ensure itâs 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 itâs 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 & Inclusion:
- 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 GoCardless:
Weâre committed to reducing our environmental impact and leaving a sustainable world for future generations. As coâfounders of the Tech Zero coalition, weâre working towards a climateâpositive future.
Data Scientist Lisbon, Portugal in London employer: GoCardless
Contact Detail:
GoCardless Recruiting Team
StudySmarter Expert Advice đ¤Ť
We think this is how you could land Data Scientist Lisbon, Portugal in London
â¨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at GoCardless. A friendly chat can open doors and give you insights that a job description just can't.
â¨Tip Number 2
Show off your skills! If you've got a cool project or a model you've built, share it. Whether it's on GitHub or a personal website, let your work speak for itself.
â¨Tip Number 3
Prepare for the interview like it's a big game! Research GoCardless, understand their fraud prevention strategies, and think about how your experience aligns with their goals.
â¨Tip Number 4
Don't forget to apply through our website! Itâs the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who take that extra step!
We think you need these skills to ace Data Scientist Lisbon, Portugal in London
Some tips for your application đŤĄ
Tailor Your CV: Make sure your CV is tailored to the Data Scientist role at GoCardless. Highlight your experience with predictive models and any relevant projects in fraud prevention or fintech. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're excited about this role and how you can contribute to our Fraud Prevention team. Be genuine and let your personality come through â we love seeing that!
Showcase Your Technical Skills: Donât forget to mention your technical skills, especially around ML and coding standards. If you've worked on production-grade models or have experience with GCP, make sure to highlight that. Weâre keen to see your hands-on experience!
Apply Through Our Website: We encourage you to apply directly through our website. Itâs the best way to ensure your application gets into the right hands. Plus, it shows us youâre serious about joining the GoCardless team!
How to prepare for a job interview at GoCardless
â¨Know Your Stuff
Make sure you brush up on your knowledge of machine learning concepts and fraud prevention techniques. Be ready to discuss your past experiences with deploying predictive models and how they made an impact. This will show that youâre not just familiar with the theory, but you can also apply it in real-world scenarios.
â¨Showcase Your Collaboration Skills
Since this role involves working closely with engineers and analysts, be prepared to share examples of how you've successfully collaborated in the past. Highlight any cross-functional projects where you turned complex data into actionable insights, as this will demonstrate your ability to work well within a team.
â¨Prepare for Technical Questions
Expect some technical questions during the interview. Brush up on your coding skills and be ready to discuss your approach to feature engineering and model deployment. Practising coding challenges or discussing your thought process on past projects can help you feel more confident.
â¨Align with Company Values
Familiarise yourself with GoCardless's values and mission. Think about how your personal values align with theirs and be ready to discuss this during the interview. Showing that you understand and resonate with their culture can set you apart from other candidates.