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Senior Market Risk Analyst- Model Validation, Stress Testing

  • Location

    London

  • Discipline:

    Credit Risk & Analytics

  • Job type:

    Permanent

  • Salary:

    £55K

  • Consultant:

    #

  • Email:

    nmohamed@merje.com

  • Job ref:

    NM/15292

  • Published:

    10 months ago

Looking for highly competent, versatile and dedicated people who can quickly get up to speed and work collaboratively to deliver within the business.

 

Key responsibilities include:

  • Independently validate, test, and challenge stress testing models, in particular, those used for capital, stress loss, revenue or RWA projections - Market Risk
  • Produce clear and high quality written model validation reports
  • Engage with model reviewers in other locations, model developers and owners and communicate the validation outcomes with the relevant stakeholders
  • Benchmark to market best practices
  • Support client proposals
  • Build and maintain strong relationships with clients and industry experts
  • Work effectively in diverse teams within an inclusive team culture where people are recognised for their contribution
 

The Person: 

  • Good understanding of stress testing methodologies as well as US, UK and EU regulatory requirements (ICAAP, CCAR, PRA, EBA) 
  • Familiar with model validation standards
  • Proficiency in all concepts of the financial derivatives market and hands-on experience with modelling of at least one asset class (i.e. IR, Credit, Equity, Commodity, FX and Inflation) in a professional environment such as Model Development, Model Validation, Quantitative Research and product control, knowledge of Structured Products is particularly beneficial.
  • Clear, concise, and structured writing and presentation skills 
  • Self-starter, highly organized and able to leverage resources 
  • Strong communication, interpersonal and stakeholder management skills 
  • Quantitative Degree (MSc or PhD) in Mathematics, Physics, Computer Science, Financial Engineering, etc;
  • Programing skills and familiarity with statistical software (e.g. R, Python, MATLAB)