
at J.P. Morgan
Bulge Bracket Investment BanksPosted 3 days ago
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**Job Title: Securitized Products Group - Business AI Strategist - Vice President** Leverage AI to revolutionize Commercial Mortgage-Backed Securities (CMBS) processes in New York. This role optimizes loan origination, deal execution, portfolio monitoring, and reporting, enhancing speed, accuracy, and decision-making. Collaborate cross-functionally to evaluate end-to-end workflows, identify bottlenecks, and build robust, integrated AI solutions. Qualify with a quantitative degree, ML experience, Python proficiency, and the ability to communicate complex ideas effectively. Preferred: CMBS/CRE experience and front-office rapid prototyping skills. Drive innovation in structured credit using AI and analytics.
- Compensation
- Not specified
- City
- New York City
- Country
- United States
Currency: Not specified
Full Job Description
Location: New York, NY, United States
Help modernize how CMBS deals get done. In this role, youll partner across banking, trading, underwriting, and technology to simplify workflows, strengthen data quality, and deploy AI-enabled tools that improve speed, accuracy, and decision support across origination, execution, and asset management.
Job summary
This role sits within the CMBS business and focuses on applying AI, analytics, and automation to improve loan origination, deal execution, portfolio monitoring, and reporting. You will evaluate current processes end-to-end, identify bottlenecks and data gaps, and build scalable solutionsintegrated into existing systemswith strong controls and human review where required. The position offers broad exposure to CRE finance and structured credit.
Job responsibilities
- Build a working understanding of CMBS and CRE deal workflows and identify high-impact opportunities to improve execution
- Design and implement ML/LLM solutions to reduce turnaround time, minimize errors, and improve insight quality
- Develop document intelligence tools to summarize and extract structured data from legal/underwriting/deal materials
- Create controlled draft-generation workflows for front-office content (e.g., investment summaries, credit memos, IC materials) with human review
- Design and maintain Python tools and data pipelines for market monitoring, deal sourcing, and scenario analysis
- Deliver recurring pipeline/portfolio/performance reporting to support investment decisions and asset management
- Partner with Technology and stakeholders to embed solutions into origination, distribution, risk, and asset management systems (including third-party data where appropriate)
Required qualifications, capabilities, and skills
- Bachelors degree or higher in a quantitative field (e.g., CS, Engineering, Data Science, Math, Finance, Real Estate)
- Production experience applying machine learning and large language models to real business workflows
- Strong Python skills; ability to build and maintain reliable tools and data pipelines
- Experience working with structured datasets (loan/bond-level, collateral, property financials) and unstructured documents (leases, rent rolls, appraisals, loan/offering documents, servicer reports)
- Ability to break down complex workflows, find root causes, and deliver scalable improvements with minimal supervision
- Strong written and verbal communication; able to explain technical topics to credit professionals and non-technical partners
- Strong partnering skills across banking/trading/underwriting/technology to deliver usable solutions at scale
Preferred qualifications, capabilities, and skills
- Experience integrating AI/analytics into front-office workflows and enterprise systems; rapid prototyping in deal-driven environments
- Prior experience in CRE origination/underwriting/securitization/surveillance or drafting structured credit investment materials
- Working knowledge of CMBS structures and CRE credit workflows (conduit and single-asset/SASB; related documentation/monitoring)
- Familiarity with CRE financing facilities (e.g., warehouse/repo), including collateral monitoring and mark-to-market processes



