Quantitative Trading & Research – Securities Services and Payments – Associate or Vice President

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

Quantitative Trading & Research team at JPMorgan Chase seeks an Associate or Vice President to apply AI/ML techniques to optimize revenue and manage risks within the Commercial and Investment Bank. The role involves developing AI/ML-driven analytics, building scalable data architectures, automating processes, and creating sequential decision-making tools. Responsibilities include end-to-end project management from brainstorming to model development, data analysis, and presenting findings to stakeholders. Requires an advanced degree in a quantitative field, strong ML/Stats/Math understanding, Python experience for data science problems, and excellent communication skills.

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
  • AI/ML applications that make business-critical predictions
  • handling vast data sets
  • optimizing decision making
  • sequential decision making tools
  • end-to-end from prototype to production

Other signals

  • AI/ML techniques
  • AI/ML applications
  • LLMs
  • deep learning