Our client, a bank is looking for a Credit Modelling Manager to strengthen the Actuarial and Risk Department. The latter is responsible for modelling and monitoring the portfolios of the company’s Finance and Risk business and the various asset classes.
The future Credit Modelling Manager will be responsible for:
- Managing the business:
- Managing a team of 6 people: federating the team on a daily basis, while contributing to its development;
- Defining the roadmap, in line with the company's strategic priorities;
- Translating the needs of the management to the employees and ensuring optimum communication/relationship/transversality between the stakeholders;
- Providing good management and monitoring of the activity, ensuring the quality of the results provided by the team.
- Supporting internal rating models:
- Developing and maintaining internal models (PD at grant and re-rating, LGD);
- Ensuring the completeness and quality of model documentation (methodologies, assumptions, results, performance measures);
- Ensuring permanent monitoring of the rating models and their performance (methodology, backtesting, etc.);
- Defending the chosen modelling approaches to internal and external audits;
- Conducting portfolio analyses to inform model behavior;
- Participating in the management and monitoring of projects and IT development requests;
- Integrating climate risk into rating models;
- Contributing to credit risk stress tests;
- Developing data science practice for management:
- Contributing to the work undertaken on the modelling of MSE risk;
- Proposing and implementing innovative approaches to better estimate and/or anticipate risks;
- Identifying use cases for machine learning techniques;
- Contributing to the choice of the methods used, and supervising the development of the models;
- Monitoring data science techniques and data that can be used for credit risk modelling.
- Graduated from ENSAE, or an engineering school, actuarial school, or a Master's degree in Statistics or Econometrics;
- Successful managerial experience;
- 8 to 12 years of proven professional experience in credit risk management or actuarial work in a banking context;
- Good knowledge of risk issues in a banking environment;
- Proven statistical and economic skills;
- Knowledge of the main Machine Learning techniques, and their framework;
- Proficiency in SAS, Python and a good understanding of IT issues;
- Good written communication and excellent interpersonal skills;
- Curiosity and proactivity;
- Sense of leadership and pedagogy.