Quantitative Trading & Research - CMBS - Analyst

JPMorgan Chase & Co.•New York, NY

1d•$110,000 - $125,000

About The Position

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.

Requirements

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 Haves

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

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