Senior Data Scientist Credit Modelling

Experteer Overview As part of the RISK Models team, you will design and develop credit risk models for corporate portfolios, using historical data to support capital calculations and risk-based decisions.You will collaborate with business lines, IT, and group modelling experts to ensure models are grounded in reality and compliant with regulations.The role offers the chance to influence risk assessment, pricing components, and credit approvals at BNP Paribas.You will operate in a cross-functional setting and contribute to the bank’s risk management strategy and data-driven transformation.Compensaciones / Beneficios- Design and develop credit risk models focused on corporate clients- Identify, source, and prepare data for modelling purposes- Collaborate with technical and business stakeholders to validate results- Document modelling steps and present options and outcomes to decision makers- Ensure proper tool implementation in cooperation with the IT department- Continuously back-test and assess model performance- Respond to external requests for credit risk statistics- Monitor regulatory developments and best-practice in credit risk modelling- Potentially expand responsibilities to HR-related tasksResponsabilidades- Master in Mathematics, Economics or Econometrics, Statistics- At least 5 years in credit risk modelling (PD/LGD/LGDd/CCF)- Proficient in English; French is a plus- Strong SAS programming skills- Asset: experience with Big Data technologies and ratings/scorings- Ability to manage long-term deadlines and work with both technical and business colleaguesRequisitos principales- Training programs, career plans and internal mobility- Diversity and Inclusion Committee- Corporate volunteering program- Flexible compensation plan- Hybrid telecommuting model (50%)- 32 vacation days

Master in Mathematics, Economics or Econometrics, Statistics

• At least 5 years in credit risk modelling (PD/LGD/LGDd/CCF)
• Proficient in English; French is a plus
• Strong SAS programming skills
• Asset: experience with Big Data technologies and ratings/scorings
• Ability to manage long-term deadlines and work with both technical and business colleaguesRequisitos principales
• Training programs, career plans and internal mobility
• Diversity and Inclusion Committee