Statistical Modeller for Credit Risk

4 tygodni temu


Warszawa, Mazovia, Polska ING Pełny etat

About the Role:

We are seeking a skilled Statistical Modeller to join our Credit Risk team. As a Statistical Modeller, you will be responsible for developing and monitoring predictive risk models, collecting data, performing analyses and reports, and interacting with stakeholders.

Key Responsibilities:

  • Develop and monitor predictive risk models, including credit decision, affordability, and Early Warning System (EWS) models.
  • Collect and manage data required for model development, monitoring, and validation.
  • Perform analyses and reports within the credit risk management area.
  • Interact with stakeholders to explain models and share knowledge.

About the Team:

Our team follows the Agile approach and mindset, using flexible frameworks like Scrum and Kanban. We are innovative and trust our colleagues. We work in small units called squads, with clear visions and autonomy to overcome challenges and work out the most effective way of working.

As a Credit Risk Modeller, you will develop statistical models to evaluate credit risk parameters at the bank. You will work in a dynamic environment at the forefront of new developments in credit risk management.

Requirements:

  • University degree in mathematics or economics with a strong focus on statistics/mathematics.
  • Experience in statistical modelling, data processing, and data quality control.
  • Proficiency in English.
  • Analytical mind, problem-solving skills, and critical thinking.
  • Good communication and team player skills.
  • Ability to clearly express ideas, facts, and opinions.
  • Ability to identify problems, analyze key information, and make connections to find solutions.
  • Ability to complete tasks efficiently, timely, and with high quality, focusing on execution and delivery of targets and KPIs.

Desirable Skills:

  • Experience with SAS and other modelling tools/languages (e.g., Python).
  • Knowledge of advanced statistical techniques, such as Bayesian modelling, Monte Carlo, and neural networks.
  • Experience in credit risk modelling and basic knowledge of credit risk management concepts.


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