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Quantitative Researcher

2 months ago

London, Greater London, United Kingdom JP Morgan Chase Bank, National Association Full time
Job Summary

We are seeking a highly skilled Quantitative Researcher to join our Markets Capital Rates team. As a key member of our team, you will be responsible for developing and maintaining sophisticated mathematical models, cutting-edge methodologies, and infrastructure to support our risk management and regulatory capital framework.

Key Responsibilities
  • Develop Mathematical Models: Design and implement mathematical models for market risk quantification across our portfolio, ensuring accuracy and reliability.
  • Research and Development: Conduct research projects to define methodologies and improve our Market Risk and regulatory capital framework, staying up-to-date with industry trends and best practices.
  • Model Implementation and Maintenance: Implement new models and maintain existing ones, ensuring seamless integration with our risk management systems.
  • Quantitative Support: Provide expert quantitative support to our trading, market risk coverage teams, and other stakeholders, ensuring timely and accurate risk assessments.
  • Collaboration and Liaison: Liaise with various functions, including Market Risk Technology, Market Risk Coverage, Markets Capital Core Analytics, Quantitative Research, and Model Risk, to ensure effective communication and coordination.
Requirements
  • Education: PhD/Masters or equivalent degree in Mathematics, Physics, Math Finance, or Engineering.
  • Programming Experience: Substantial Python programming experience, with a strong understanding of derivatives valuation models.
  • Analytical and Problem-Solving Skills: Excellent analytical and problem-solving abilities, with a keen eye for detail and a passion for solving complex problems.
  • Communication Skills: Excellent communication skills, both verbal and written, with the ability to effectively communicate complex technical concepts to non-technical stakeholders.
Preferred Qualifications
  • Derivatives Pricing Knowledge: Strong knowledge of rates derivative pricing, probability theory, stochastic processes, partial differential equations, numerical analysis, statistics, time series analysis, and machine learning.
  • Quantitative Risk Modeling: Experience with quantitative risk modeling, including VaR, risk capital models, and stress methodology.
  • Model Development or Validation: Experience in model development or model validation, with a strong understanding of model risk management.