Wholesale Credit Risk Portfolio Analytics - Associate

JPMorgan Chase JPMorgan Chase · Banking · LONDON, United Kingdom · Corporate Sector

This role focuses on applying large language model techniques to synthesize structured insights from unstructured data within the wholesale credit risk domain. The primary focus is on data preparation and analysis (L0), with a secondary application in agentic systems for insight synthesis (L4). The role requires strong quantitative skills, proficiency in Python and SQL, and experience in financial risk analytics.

What you'd actually do

  1. Produce recurring and ad hoc portfolio analytics, including concentrations, rating migration, emerging risks, and performance trends
  2. Transform large datasets into actionable insights for credit officers and senior stakeholders
  3. Conduct thematic deep dives on priority risk topics
  4. Design and execute stress testing and sensitivity analyses across scenarios
  5. Develop and refine industry models to forecast financial outcomes based on macroeconomic indicators

Skills

Required

  • Bachelor’s or Master’s degree in Mathematics, Statistics, Economics, Finance, or a related quantitative field
  • Experience in financial risk analytics, credit risk management, or data science
  • Proficiency in Python, R, or SQL
  • Strong analytical and problem-solving skills
  • Ability to work with large and complex datasets
  • Strong written and verbal communication skills
  • Ability to present complex analysis in a clear and structured manner
  • Strong collaboration and teamwork skills
  • High attention to detail and adaptability
  • Ability to build relationships with internal stakeholders

Nice to have

  • Experience in quantitative modeling using Python
  • Experience in publishing industry research or analytical reports
  • Interest in applying modern technologies within financial services

What the JD emphasized

  • Apply large language model techniques to synthesize structured insights from unstructured data

Other signals

  • applying modern technologies within financial services
  • Apply large language model techniques to synthesize structured insights from unstructured data