Our client is on the trend of greater use of models. Driven in part by regulation but the growing reliance on models manifests in all areas of the company. For risk management purposes the company has models in place for a.o. credit risk, market risk, operational risk, and liquidity risk, covering the entire balance sheet. These models use market and client data to predict among others, the client behaviour and their risk profile based on the client characteristic, economy and market.
Outside the risk management domain, model-based decision making becomes increasingly important and accepted. Models are also used for pricing, marketing, portfolio management, HR and in multiple other areas and innovative solutions. The Model Validation team is the main party challenging the quality of the models in the company. As a result of the increase of the usage of models, their scope is continuously expanding.
This increase requires also challenging models with new techniques, causing a stimulating and dynamic work environment. Do you enjoy an analytic job which makes a difference in the organization? Do you seek for a challenging and dynamic job with the opportunity of steep learning? Join this team aimed to manage the companies model risk in the best possible way.
About the job
As a Model Validator in this company, you can become an expert in analyzing and critically challenging several categories of risk models and get the understanding of the related risks. On the way, you will learn how to assess the quality of data; challenge state-of-the-art modelling techniques and/or checking the implementation and use of models. A Senior Model Validator gets the opportunity to challenge it all!
Your job Model Validation covers all risk models; for instance credit risk, market risk, counterparty credit risk, interest rate risk, liquidity risk and so on. On top of that, multiple other model categories are being validated. In this particular industry this is a unique setting. It will stretch your learning curve across a wide variety of modelling techniques, risk types, businesses and stakeholders. Furthermore, Model Validation performs as a centre of expertise by sharing knowledge on modelling, validation, model risk management and regulatory compliancy with all our stakeholders. The task of a Validator comes with a large amount of responsibility.This responsibility also requires managing a wide range of key relationships of the Model Validation department.
Your working environment Model Validation operates independently of the model development departments at the company to ensure the objectivity of the validation process. It covers the model risk dimensions of data, methodology, implementation and use. The outcome of the validation process affects every level of the organisation – from individual client acceptance to strategic decision making and steering. It is a vital team for assuring model driven decisions are of good quality. This responsibility is shared across the team of about 25 specialists in a very international and diverse environment. This diversity, in terms of cultural background, gender, academic and working experience creates an optimal blend. We value team players who are smart, persistent, take their role seriously and are committed to finish the job.
Your profile
University degree in a quantitative discipline, e.g. (financial) mathematics, (theoretical) physics, econometrics or similar, at least at Master level. A PhD and/or additional. Qualification (e.g. second Master degree in economics, finance or similar) is desirable.
At least 2 years of relevant work experience in a quantitative role in the financial industry (e.g. modeller, model validator, quantitative risk manager, quant developer, quantitative consultant) and/or in related research.
Knowledge of financial risk measurement and management methodologies.
Knowledge of regulatory requirements regarding model risk management and (risk) modelling.
Knowledge and experience with mathematical finance, statistics and econometrics.
Knowledge of financial markets & products.
Experience with modern programming languages, e.g. Python, MATLAB, C++ and/or database tooling, e.g., SQL, SAS and their application in statistical analysis.
Good communication and influencing skills to a wide range of stakeholders
