at J.P. Morgan
Bulge Bracket Investment BanksPosted 3 days ago
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**Quantitative Trading & Research - CMBS - Analyst** in New York drives CMBS deal lifecycle modernization via AI, from origination to asset management. Primary duties include: - **Workflow analysis** and automation targeting, gaining expertise in CMBS and CRE deal processes - **Design and deployment of ML/LLM solutions**, enhancing deal speed and accuracy - **Document summarization and data extraction tools** creation for unseen structured and unstructured data - **Python tools and pipelines** for market monitoring, sourcing, analysis, and reporting - **Solution embedding** into origination, distribution, risk, and asset management systems, collaborating with tech and business stakeholders /bashimiilar 3 years in quantitative roles, requiring a quantitative degree, strong Python skills, and data familiarity. Essential is the ability to simplify complex workflows and communicate effectively across teams. **Preferences**: Experience in front-office AI/analytics workflows, CMBS/CRE expertise, and AWS proficiency.
- Compensation
- Not specified
- City
- New York City
- Country
- United States
Currency: Not specified
Full Job Description
Location: New York, NY, United States
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
