Fraud Risk Data Analyst

Thanks to our innovative BuyNow PayLater payment solution, Scalapay is transforming the way more than 6.5 million customers buy online and in-store, empowering 8,000+ merchants (online and in-store) to give their customers magical shopping experiences. 

Being only 3 years old didn’t stop us from becoming a unicorn 🦄 We have raised over $700mln and we did this thanks to a team built around our 4 core values: #MakeItHappen #PlayAsATeam #StayCurious #FocusOnCustomer.

This is where your magic happens. If you love it, Scalapay it ♥

The Mission

We’re looking for an enthusiastic Fraud Risk Data Analyst to join our Risk & Analytics team and help protect Scalapay and its customers from fraud. In this role you’ll turn raw data into actionable insights, help refine fraud-detection models, and partner with product, engineering and data-science teams to minimise fraud losses while keeping checkout friction-free.

• Key Responsibilities

  • Own fraud data pipelines & dashboards – build, monitor and continuously improve metrics that track fraud attack rates, false-positive rates and rule/model performance.
  • Deep-dive analyses – mine transaction, device and behavioural data to uncover emerging fraud patterns, root causes and growth opportunities.
  • Model & rule optimisation – collaborate with data scientists to design, validate and recalibrate machine-learning models, features and rule sets.
  • Real-time monitoring & alerting – design automated alerts that surface anomalous fraud spikes and enable rapid response.
  • Tooling & enrichment – integrate third-party fraud signals and internal data sources to enhance feature sets and decision accuracy.
  • Stakeholder reporting – translate complex analyses into clear, data-driven insights and recommendations for Risk, Product and Leadership teams.
  • Investigation support – provide advanced querying and statistical support for escalations, chargeback disputes and post-mortems.

Who We're Looking For:

Must Have

• Bachelor’s in Statistics, Mathematics, Computer Science, Engineering, Economics or a related quantitative field.
• Solid grasp of fraud-risk concepts in payments, fintech or e-commerce.
• Proficiency in SQL (window functions, CTEs, optimisation) for large-scale data extraction and analysis.
• Working knowledge of Python (pandas, numpy, scikit-learn or similar) for data wrangling and exploratory analysis.
• Analytical mindset, strong problem-solving skills and relentless attention to detail.
• Ability to prioritise in a fast-paced environment and communicate insights clearly in English, both written and verbal.

Nice to Have

• Experience with cloud data warehouses (Snowflake, BigQuery, Redshift) and BI tools (Looker, Tableau, Power BI).
• Exposure to machine-learning workflows (feature engineering, model evaluation, MLOps).
• Familiarity with fraud-detection platforms or graph-analysis techniques.
• Prior internship or role in fraud risk, data analytics or financial services.

Why you should join Scalapay:

• International environment with significant challenges to be met every day
• Lots of opportunities to work with a team of industry tech leaders who are focused on delivering products that offer exceptional user experiences
• Personalised support to accelerate your professional growth and take ownership of the products you deliver: we want to help you grow!
• Latest technologies and being encouraged to bring your flair to the role.

Recruitment Process:

1. A quick chat with one of our Talent Acquisition team members
2. The first interview with the Hiring Manager to dive deep into your experiences and better understand your motivation
3. A case study to test your hard skills
4. A final chat with Simone, our CEO

Want to learn more? Don't hesitate to explore our Careers website, our LinkedIn and Glassdoor pages. 

Pro tip: send your CV in English 😉

Super Pro tip: we know that application processes can be scary and frustrating but… we look for talent, not people that tick all our boxes.

We believe in the power of diversity: Scalapay is an Equal Opportunity Employer for any minority, disability, gender identity or sexual orientation.