Quantitative Trading & Research - Valuation Models - Vice President

JPMorgan Chase JPMorgan Chase · Banking · LONDON, LONDON, United Kingdom · Commercial & Investment Bank

Develop and maintain sophisticated models for fair value measurement across multiple business lines, leveraging machine learning, AI, and data analytics to enhance valuation methodologies and drive innovation. Implement methodologies for model calibration and build analytics to manage model risk appetite.

What you'd actually do

  1. Develop mathematical models for the valuation of credit derivatives, structured lending facilities, and illiquid collateral
  2. Implement methodologies for model calibration and build analytics to manage model risk appetite
  3. Leverage machine learning, AI, and data analytics to enhance valuation methodologies and drive innovation
  4. Define methodology for pricing adjustments, model limitations, parameter uncertainty, and liquidity reserves
  5. Monitor model performance metrics to ensure models behave as expected over time

Skills

Required

  • Demonstrated quantitative and problem-solving skills, including research abilities
  • Strong understanding of advanced mathematics in financial modeling (probability theory, stochastic calculus, statistics)
  • Hands-on experience with data analytics, large data sets, and tools for analysis and visualization
  • Proficiency in code design and programming, primarily Python and C++
  • Practical experience with code performance optimization, debugging, and reverse engineering
  • Excellent verbal and written communication and team skills in a multi-location environment
  • Deep understanding of financial products, their valuations, and associated risks

Nice to have

  • Advanced degree (PhD, MSc or equivalent) in Engineering, Mathematics, Physics, Computer Science, etc.
  • Proven quantitative model development or model validation experience
  • Markets experience and familiarity with trading concepts and terminology
  • Knowledge of options pricing theory, trading algorithms, financial regulations, stochastic processes, partial differential equations, and numerical analysis

What the JD emphasized

  • Deep understanding of financial products, their valuations, and associated risks

Other signals

  • Leverage machine learning, AI, and data analytics to enhance valuation methodologies and drive innovation
  • Develop mathematical models for the valuation of credit derivatives, structured lending facilities, and illiquid collateral
  • Implement methodologies for model calibration and build analytics to manage model risk appetite