Quantitative Trading & Research - CMBS - Analyst
Job in
City of Rochester, Rochester, Monroe County, New York, 14602, USA
Listed on 2026-07-16
Listing for:
JPMorgan Chase & Co.
Full Time
position Listed on 2026-07-16
Job specializations:
-
Finance & Banking
Job Description & How to Apply Below
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 SummaryAs 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
- 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
- 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
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