Quantitative Model Developer (f/m/d)

In this role you will maintain and enhance quantitative risk models used for credit and risk management within a group-wide risk function. You will own the end-to-end model lifecycle—from development and calibration to monitoring and reporting—working closely with IT and stakeholders. You’ll provide management information and support regulatory activities, contributing to prudent risk management aligned with the institution’s credit appetite. This position offers the opportunity to shape rating methodologies and impact senior decision-making. You will operate in a collaborative, regulation-driven environment with a focus on accuracy and continuous improvement.

• Develop, maintain and continuously improve quantitative rating and risk models for credit and risk management
• Document and manage processes for model maintenance; drive ongoing improvements and address findings from monitoring/validation
• Regularly assess model adequacy, perform calibrations, impact assessments and report results
• Collaborate with model users and IT to support IT development, write business requirements and conduct user acceptance testing
• Deliver insightful management information for senior management and committees
• Maintain the internal model inventory and support routine tasks
• Foster effective relationships with internal stakeholders and regulatory authorities

• Master's degree in a quantitative field (mathematics, computer science, physics or related)
• 2-3 years of end-to-end experience in development, implementation, and validation of credit rating models
• Strong quantitative risk modelling expertise in creditworthiness assessment and rating assignment (logistic regression, scorecards, ML techniques)
• Solid understanding of regulatory requirements for rating models (CSDR, CRR, BCBS, MaRisk) and ability to prepare documentation for regulatory submissions
• Proficient programming skills in Python (NumPy, SciPy, Pandas, PySpark) and familiarity with development tools (Azure Databricks, GitHub)
• Ability to analyze large datasets, identify data quality issues, and derive actionable insights
• Meticulous attention to detail, strong analytical and problem-solving skills
• Excellent English communication; German/French as an asset

• meticulous attention to detail
• strong analytical and problem-solving abilities
• team spirit and collaboration across functions
• logistic regression
• scorecard development
• machine learning techniques for credit risk