STA Model Validator IRRBB & ICLAAP

ING

Amsterdam

On-site

EUR 55,000 - 90,000

Full time

14 days+
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Job summary

ING in Amsterdam seeks an energetic, collaborative Model Validator for a 4-month short-term assignment within Model Validation Financial Risk. The role focuses on validating Economic Capital models and requires strong quantitative background with relevant experience in risk modelling and regulatory frameworks.

You will conduct validations, challenge risk metrics, prepare reports, and present findings to committees while coordinating with various teams to ensure consistency across locations and

Qualifications

  • MSc or PhD in a quantitative field with relevant experience in model validation.
  • Adequate technical and regulatory expertise in quantitative modelling and risk management.
  • Excellent attention to detail and commitment to high-quality work.
  • Strong verbal and written communication; able to explain complex topics to diverse stakeholders.
  • Ability to challenge 1st MLoD on model risk and manage multiple priorities.

Responsibilities

  • Conduct timely, high-quality model validations in line with external regulations and internal policies.
  • Challenge 1st MLoD on their level of model risk.
  • Prepare validation reports and present findings to committees and stakeholders.
  • Collaborate across chapters and locations to ensure consistency and share best practices.
  • Maintain constructive relationships with internal teams and external regulators/auditors.

Skills

Quantitative background
Regulatory modelling expertise
Attention to detail
Strong communication

Education

MSc or PhD in Econometrics/Financial Mathematics/Quantitative Finance/Mathematics/Statistics/Physics

Tools

Model Validation Frameworks

Job description

We are looking for an energetic, naturally collaborative, and hands-on Model Validator to support the Chapter Lead IRRBB & ICLAAP within Model Validation Financial Risk in a 4-month Short Term Assignment with focus on validation of Economic Capital models.

In particular, as a member of our team, you have:

  • A quantitative background, i.e. a MSc or PhD degree in e.g. (Financial) Econometrics, Financial Mathematics, Quantitative Financial Economics, Mathematics, Statistics, Physics etc. combined with significantly relevant professional experience in a comparable role

  • Adequate technical and regulatory expertise in quantitative modelling and risk management.

  • Attention to detail and commitment to delivering high-quality work.

  • Excellent verbal and written communication skills, with the ability to explain complex quantitative and regulatory topics clearly and convincingly to a broad range of stakeholders, including senior management, auditors and regulators.

  • Ability to challenge 1st MLoD on their Model Risk and to manage multiple priorities in a fast-changing environment.

Specific Tasks and Responsibilities
  • Conduct timely, high-quality model validations in line with external regulations, internal policies, and model validation frameworks.

  • Challenge 1st MLoD on their level of model risk

  • Prepare validation reports and present findings to committees and stakeholders, ensuring transparency and clarity.

  • Collaborate with colleagues across chapters and locations to ensure consistency and share best practices.

  • Maintain constructive relationships with internal teams (Model Development, Risk Management, Audit) and assist with external stakeholder management (regulators, auditors).

Within your assignment your model scope is related to Market Risk Economic Capital models, hence knowledge on the following areas is deemed important:

  • Behavioral: Modelling of mortgage & other loan prepayments and non-maturity deposits, as well as other embedded options in retail and wholesale products.

  • Interest rate: Market models (e.g. Hull-White model).

  • Valuation: Models used for calculating the economic value of a loan or deposit portfolio with embedded options.

  • Risk measurement: Models used for risk management and reporting (NPVaR, RRaR, NIIaR).

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