Risk Data Scientist
Pubblicato il 10-10-2026 - Scalapay in Milano
At Scalapay, we’re shaping a culture with high standards, independent and critical thought, innovation, ownership, and continuous learning . We operate in a fast-moving, tech-driven environment , and we’re looking for people who thrive in change, think boldly, and take initiative .
If you’re ready to put your potential to the test in a hiring process designed to spotlight exceptional talent, this is your chance to stand out and grow with one of Europe’s most ambitious fintech teams .
MakeItHappen #PlayAsATeam #StayCurious #FocusOnCustomer.
THE MISSION:
As a Risk Data Scientist , you will own the design, implementation, and oversight of credit & fraud model and reporting . This is a high-impact role where you will collaborate with cross-functional teams to align on product and risk strategy changes to understand impact on loss performance and approval rates.
This role will ensure accurate projections of portfolio performance, early detection of risk trends, and actionable insights to drive business decisions .
WHAT YOU WILL DO:
- Develop, implement,
and maintain Credit Risk & Fraud models across the BNPL & Lending portfolio.
- Build analytical tools and produce analysis to find Risk & Losses drivers.
- Monitor approval rates, fraud & credit performance and recovery trends.
- Build scalable BI dashboards
Business Intelligence & Reporting
- Own the design, automation, and delivery of Risk models & BI reporting for leadership and stakeholders.
- Partner with cross functional areas like Finance, payments, product risk to provide data-driven insights into credit losses, profitability, and risk-adjusted returns.
- Ensure regulatory and management reporting requirements are met with high accuracy.
Cross-Functional Collaboration
- Work closely with credit risk, data science, finance, and product teams to align loss assumptions with pricing, provisioning, and strategic planning.
- Communicate findings and recommendations to senior leadership and external stakeholders (e.g.,