Credit Risk Modeler

ING Hubs Romania

Lombardia

On-site

EUR 70,000 - 110,000

Full time

6 days ago
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Job summary

ING Italy is seeking Credit Risk Modellers with at least 4 years of experience in AIRB modelling for Wholesale Banking portfolios. The role focuses on development and calibration of credit risk models, in collaboration with ING Group’s WB Model Development team.

You will work across products and regions, applying ING's modelling standards while interacting with regulators and front office colleagues to support risk decisions.

Qualifications

  • Academic degree (MSc or PhD) in a quantitative field.
  • Strong knowledge of Basel and IFRS 9 models.
  • 4+ years developing and monitoring Wholesale Banking Credit Risk Models.
  • Experience with data modelling and coding in listed languages.

Responsibilities

  • Develop and maintain credit risk models for Wholesale portfolios.
  • Advise front office on credit risk decisions and model usage.
  • Interact with regulators on model design, submission and updates.
  • Improve measurement and monitoring of existing models.

Skills

English (fluent)
Analytical skills
Problem-solving
Communication skills
Execution skills
Team player
Creative and proactive mindset

Education

MSc or PhD in econometrics, physics, statistics or mathematics

Tools

C++
Java
Python
R
SAS

Job description

NG Italy is looking for Credit Risk Modellers that have at least 4 years of experience with developing AIRB models for Wholesale Banking portfolios. You will be working for and in close cooperation withthe WB Credit Risk Model Development department of ING Group. The WB Credit Risk Model Development department is part of ING Group Model Development which comprises of a large team of modelling experts in Trading Risk, Credit Risk and Market Risk in IRRBB and Balance Sheet Risk models and that uses state-of-the-art modelling methods, tooling, and data-processing technologies.

You will be joining an energetic team of highly qualified professionals.

Our area of expertise is modelling Credit Risk and IFRS 9 concepts for ING Wholesale Banking. These models are core to the success of ING's corporate lending activities!

The position offers excellent opportunities to excel in what you do and to broaden your modelling and coding skills, as well as exposure to a dynamic and agile international working environment.

Doesit sound interesting? Please read on!

What you'll do

The team activities are quite varied - here are some of the main ones:

  • You will play a crucial role in the development and maintenance of models for measuring and managing credit risk for Wholesale portfolios, as well assteering and advising the front office colleagues when taking credit risk decisions.
  • You will interact with different regulators with regard to to the design, use and implementation of the models, as well on leading/ managing discussions on changes to the models and its submissions.
  • You will take responsibility for developing and calibrating credit risk models by applying ING's modelling standards. The models that you develop will cover all products and geographical regions in the ING Bank portfolio.
  • In addition to the modelling activities, you will also be responsible for further improving the measurement and monitoring of existing models.

Who we are looking for

  • You have an academic degree (MSc or PhD), preferably in econometrics, physics, statistics, or mathematics
  • You have good knowledge of Basel and IFRS 9 models and experience in developing expert-based or statistical credit risk models
  • You have at least 4 years of relevant experience in developing and monitoring regulatory Wholesale Banking Credit Risk Models
  • You have extensive experience in using data modelling software/ or coding (C++, Java, Python, R, SAS)
  • You are a sparring partner/advisor to Senior Management
  • You have strong analytical, problem-solving, communication and execution skills
  • You have a creative and pro-active mind-set
  • You can challenge the status quo
  • You are a team player
  • You are fluent in English
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