What are the responsibilities and job description for the Quantitative Trading & Research - CMBS - Analyst position at JPMorgan Chase?
Modernize how CMBS deals get done. Partner across Banking, Trading, Underwriting, and Technology to streamline workflows, strengthen data quality, and deploy AI-enabled tools that accelerate origination, execution, and asset management.
Job Summary
As an Analyst on the Quantitative Trading & Research team, you will be an embedded Quantitative Strategist within the CMBS business, applying AI engineering, analytics, and automation across the full deal lifecycle — origination, execution, portfolio monitoring, and reporting. You will have direct exposure to senior investment professionals and broad coverage of CRE finance and structured credit markets.
Job Responsibilities
- Build working knowledge of CMBS/CRE deal workflows; target high-impact automation opportunities
- Design and deploy ML/LLM solutions that reduce turnaround time, minimize errors, and sharpen analytical insight
- Build document-intelligence tools to summarize and extract structured data from legal, underwriting, and ac Materials
- Deliver controlled draft-generation workflows for front-office content (investment summaries, credit memos, IC materials) with human-in-the-loop review
- Build Python tools and data pipelines for market monitoring, deal sourcing, scenario analysis, and portfolio performance reporting
- Partner with Technology and business stakeholders to embed solutions into origination, distribution, risk, and
- asset-management systems, including third-party data platforms
Required Qualifications, Capabilities, and Skills
- 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
Preferred Qualifications, Capabilities, and Skills
- 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