Senior Data Scientist – Credit Risk
Location: Milan, Italy
Working Pattern: Hybrid (3 days per week in the office, 2 days WFH)
Salary: €95,000–€145,000 plus premio and benefits
Employment Type: Full-time, Permanent
About the Role
We are seeking an experienced Senior Data Scientist to join a growing Credit Risk Analytics team in Milan. This role will focus on developing and deploying advanced machine learning and statistical models that drive credit decisioning, portfolio management, and risk strategy.
The successful candidate will combine deep technical expertise with strong business acumen to influence lending decisions and deliver measurable commercial impact. The position offers a hybrid working model with three days per week in the Milan office and two days working remotely.
Key Responsibilities
- Develop, validate, and monitor credit risk models across the customer lifecycle.
- Build Probability of Default (PD), loss forecasting, and credit scoring models using modern machine learning techniques.
- Analyse portfolio performance, default trends, and risk drivers to support strategic decision-making.
- Partner with Credit Risk, Product, Engineering, and Commercial teams to implement data-driven solutions.
- Design and execute model performance monitoring and challenger frameworks.
- Translate complex analytical findings into actionable recommendations for senior stakeholders.
- Ensure model governance, documentation, and regulatory compliance standards are met.
- Mentor junior data scientists and contribute to best practices across the data science function.
Requirements
- 5+ years' experience in Data Science, Machine Learning, or Quantitative Analytics.
- Strong experience within credit risk, lending, consumer finance, banking, fintech, BNPL, or credit scoring environments.
- Proven track record developing and deploying credit risk models in production environments. Credit-scoring, PD modelling, and portfolio analytics experience are commonly sought by leading employers in Milan's banking and fintech sector.
- Advanced Python and SQL skills.
- Experience with machine learning libraries such as scikit-learn, XGBoost, LightGBM, or similar.
- Strong understanding of model validation, calibration, monitoring, and explainability techniques.
- Excellent communication skills and the ability to engage with both technical and non-technical stakeholders.
- Degree in Statistics, Mathematics, Computer Science, Economics, or a related quantitative discipline.
Desirable Experience
- IFRS 9, Basel, or regulatory risk modelling experience.
- Experience with cloud platforms (AWS, Azure, or GCP).
- Exposure to real-time decisioning systems and automated underwriting.
- Knowledge of credit bureau, open banking, or alternative data sources.
What’s on Offer
- Hybrid working model (3 office / 2 remote).
- Opportunity to shape credit risk strategy within a data-driven organisation.
- Modern technology stack and strong investment in analytics.
- Career progression into Lead Data Scientist or Head of Data Science pathways.
- International and collaborative working environment.