Hybrid Capital Modelling Actuary – Credit Risk

Hybrid Capital Modelling Actuary – Credit Risk

Full-Time 49500 - 60500 Β£ / year (est.) Home office (partial)
Oliver James Associates Ltd.

At a Glance

  • Tasks: Develop internal models and frameworks for credit risk in a dynamic team.
  • Company: Join a leading UK life insurer with a focus on innovation.
  • Benefits: Competitive salary, flexible working options, and professional growth opportunities.
  • Other info: Exciting role in London with a collaborative and supportive work culture.
  • Why this job: Make a real impact in the financial sector while advancing your actuarial career.
  • Qualifications: Qualified actuary with experience in capital modelling and credit risk.

The predicted salary is between 49500 - 60500 Β£ per year.

Oliver James Associates Ltd. partners with a specialist UK life insurer to recruit a Capital Actuary in the Financial, Capital Insights & Actuarial team.

The role focuses on credit risk, developing internal models, capital frameworks and market risk methodologies in London.

The ideal candidate is a qualified actuary with capital modelling and credit risk experience, skilled in Python, R or Matlab, and familiar with Solvency II UK.

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Hybrid Capital Modelling Actuary – Credit Risk employer: Oliver James Associates Ltd.

Oliver James Associates Ltd. is an excellent employer, offering a dynamic work environment within a long-established London Market Insurance firm that values flexibility and employee well-being. With opportunities for professional growth and development in risk management, employees benefit from a supportive culture that encourages collaboration and innovation, making it an ideal place for those seeking meaningful and rewarding careers in the heart of London.

Oliver James Associates Ltd.

Contact Details:

Oliver James Associates Ltd. Recruitment Team

We think you need these skills to ace Hybrid Capital Modelling Actuary – Credit Risk

Capital Modelling
Credit Risk Experience
Python
R
Matlab
Solvency II UK
Internal Models Development