Credit Risk Modelling Analyst

£55000 - £60000 per annum


£55,000 - £60,000


Are you looking to work in a credit risk analyst role that involves a lot of technical work modelling in SAS or SQL? This position is the perfect opportunity for a credit risk analyst that wants to work using classical modelling and machine learning to develop predictive models.


The company is a financial institution based in London. The company is a market leader that has been growing for the last 10 years and this is a really exciting time to join the company as they continue to expand.


The position sits in the credit risk modelling. The main aspect of the role will be developing predictive models and credit risk scorecards. There will also be an element of the role that requires you to explain and defend the models you have developed to senior stakeholders.


  • Knowledge of IFRS9 and IRB regulations
  • Experience using SAS or SQL to build credit risk models
  • A statistics degree or relevant statistics experience
  • Experience working on credit risk strategies
  • Good communication skills


  • £55,000 - 60,000 base salary
  • Standard benefits


Please register your interest by sending your CV to Dapo Agiri via the Apply link on this page.

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How To Get Started In Risk Analytics

Risk Analytics has been an integral part of teams across several industries for years. After the 2008 financial crash, whereby $8 trillion was wiped from the stock market’s value in the space of two days in the US alone, the need for businesses to be savvier and more ‘switched on’ to the potential downturns and crises the economy may face was imperative. The kind of devastation the financial crash caused in a matter of days had knock-on effects to businesses of all shapes and sizes for years afterwards, and nobody could risk the same level of destruction again.  For a long time prior to this key event, it wouldn’t be an exaggeration to suggest that a lot of business owners and C-Suite executives depended on gut instinct to make critical business decisions. But, as we began to enter not only a more economically turbulent time but also an era that became dominated by technology, the need for hard evidence to support ‘intuition’ was crucial. 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