Quantitative Trading & Research – Securities Services, Payments and Cib Treasury - Associate

JPMorgan Chase JPMorgan Chase · Banking · New York, NY +1 · Commercial & Investment Bank

Quantitative Trading & Research team at JPMorgan Chase seeks an Associate/VP to apply AI/ML techniques, including LLMs and sequential decision making, to transform business operations in Securities Services, Payments, and CIB Treasury. The role involves end-to-end project management, from data processing and analysis to model development and presenting commercial recommendations.

What you'd actually do

  1. Work with business leads to develop AI/ML-driven analytics and automation that support their business goals
  2. Build up a scalable data architecture to handle the large volume of transaction data
  3. Automate existing manual processes and build tools to enable the business to optimize their decision making and deposit management
  4. Build sequential decision making tools to optimize the net interest income of the business under various liquidity, capital and balance sheet constraints
  5. Drive projects end-to-end, from brainstorming, prototyping, data processing, data analysis to model development

Skills

Required

  • Advanced degree (PhD or MS) or equivalent in a quantitative field: Physics, Mathematics, Computer Science, Engineering, etc
  • Robust understanding of Machine Learning, Statistics, and Mathematics, both in fundamentals as well as in application
  • Experience in tackling real world data science problems, end-to-end from prototype to production, using Python
  • Excellent communication skills (both verbal and written) and the ability to present findings to a non-technical audience

Nice to have

  • Participation in KDD/Kaggle competition, Hackathons or contribution to GitHub
  • You demonstrate hands-on experience in solving sequential decision making problems
  • Experience in applying LLMs and/or deep learning methods to solve business problems
  • Experience in working with Cloud and/or HPC environments

What the JD emphasized

  • tackling their most technically complex business problems
  • leveraging LLMs to deliver capabilities at scales never before possible
  • developing ML applications that make business-critical predictions
  • handling vast data sets
  • tight partnership with the business in identifying their most pressing pain points and iterating towards a solution that really works for them
  • end-to-end from prototype to production
  • sequential decision making problems
  • applying LLMs and/or deep learning methods to solve business problems

Other signals

  • LLMs
  • ML applications
  • sequential decision making