Quantitative Trading & Research - Cmbs - Analyst

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

This role focuses on applying AI engineering, analytics, and automation to modernize CMBS deal workflows within JPMorgan Chase. The analyst will design and deploy ML/LLM solutions for tasks like document intelligence, data extraction, content generation, and market monitoring. They will build tools and data pipelines, and partner with technology and business stakeholders to embed these solutions into existing systems. The role requires strong Python skills, experience with structured and unstructured data, and familiarity with LLM integration patterns like vector databases, fine-tuning, evaluation frameworks, and RAG.

What you'd actually do

  1. Build working knowledge of CMBS/CRE deal workflows; target high-impact automation opportunities
  2. Design and deploy ML/LLM solutions that reduce turnaround time, minimize errors, and sharpen analytical insight
  3. Build document-intelligence tools to summarize and extract structured data from legal, underwriting, and ac Materials
  4. Deliver controlled draft-generation workflows for front-office content (investment summaries, credit memos, IC materials) with human-in-the-loop review
  5. Build Python tools and data pipelines for market monitoring, deal sourcing, scenario analysis, and portfolio performance reporting

Skills

Required

  • Bachelor's or higher in a quantitative discipline (CS, Engineering, Data Science, Finance, Real Estate)
  • 3+ years as a quantitative strategist or in a related role — quantitative finance, data engineering, or applying ML/LLMs to production workflows
  • Strong Python; proven ability to build reliable tools and pipelines on a centralized data warehouse and platform framework
  • Hands-on with structured and unstructured data; familiarity with vector databases, fine-tuning, evaluation frameworks, and RAG/MCP patterns for LLM integration
  • Ability to decompose complex workflows, identify root causes, and deliver scalable improvements with minimal supervision
  • Strong written and verbal communication; able to convey technical concepts to credit and non-technical partners
  • Strong cross-functional partnering across Banking, Trading, Underwriting, and Technology

Nice to have

  • Experience embedding AI and analytics into front-office workflows on AWS and enterprise systems; rapid prototyping in deal-driven environments
  • Working knowledge of and background in CMBS/CRE origination, underwriting, securitization, surveillance, or structured credit modeling
  • Familiarity with CMBS relative value analytics, financing facilities, collateral monitoring, and mark-to-market

What the JD emphasized

  • applying AI engineering, analytics, and automation
  • Design and deploy ML/LLM solutions
  • Build document-intelligence tools
  • Deliver controlled draft-generation workflows
  • Build Python tools and data pipelines
  • embed solutions into origination, distribution, risk, and asset-management systems
  • applying ML/LLMs to production workflows
  • vector databases, fine-tuning, evaluation frameworks, and RAG/MCP patterns for LLM integration

Other signals

  • Deploying AI-enabled tools
  • Applying AI engineering, analytics, and automation
  • Design and deploy ML/LLM solutions
  • Build document-intelligence tools
  • Deliver controlled draft-generation workflows
  • Build Python tools and data pipelines
  • Embed solutions into origination, distribution, risk, and asset-management systems