Quantitative Analyst - Model Validation Market Risk (Trading)

We are looking for you, if you have:

• quantitative background, (MSc or PhD degree) in e.g. Econometrics, Quantitative Methods, Quantitative Finance, Mathematics, Statistics, or Physics,
• good knowledge of financial engineering, statistics, mathematics, econometrics, and/or probability,
• good knowledge of market risk models e.g. VaR, Expected Shortfall, FRTB) and/ or counterparty credit risk (e.g. SIMM, CVA, PFE calculation),
• knowledge of financial products (e.g. bonds) and financial derivatives (options, forwards, interest rate swaps etc.),
• the ability to clearly, succinctly, and confidently express complex ideas, facts, and opinions. You can communicate them fluently, logically, and in the English language, both in speaking and writing, supported by appropriate tools (plots, tables, data, etc.),
• the ability to identify hidden problems, analyze key information, and form intelligent connections to find appropriate solutions,
• the ability to work well with others and are interested in your team’s success as much as your own.

English level: C1

You'll get extra points for:

• experience in quantitative model validation or model development in the area of Trading Risk or generic Market Risk,
• a research mindset,
• knowledge of Python,
• certificates: FRM, PRM or CQF.

Your responsibilities:

• Model analysis,
• Writing validation reports,
• Development of tools for automation of validation process,
• Alignment with model developers and model owners,
• Learning the latest developments in trading risk models domain.

Information about the team:

The Trading Risk Model Validation Tribe has 60 experts and specialists split into 3 chapters in Amsterdam and 2 chapters in Warsaw. The Warsaw chapters have 20 validators, which work as a one team with a central team located in Amsterdam.

We are responsible for validating market risk, counterparty credit risk and valuation models for trading books used by ING Group worldwide. Our core mandate is to address whether a particular model is fit for its designated purpose, based on mathematical assumptions, appropriate business contexts, academic theories, and empirical evidence, and is properly adherent to regulations, best practices, and the latest technological innovations.