Securitized Products Group - Quantitative Strategist (ML / LLM) - Tax Oriented Investment Associate

JPMorgan Chase & Co.•Chicago, IL

4d•$150,000 - $200,000

About The Position

The Tax Oriented Investments team is seeking a highly analytical, technically strong Associate to apply machine learning (ML) and large language models (LLMs) to front-office investment workflows. This role sits at the intersection of quantitative research, AI engineering, and deal execution, focused on modernizing deal origination, underwriting, syndication, and asset management through automation, analytics, and AI-enabled decision support.

Requirements

Bachelor’s or advanced degree in Computer Science, Engineering, Data Science, Mathematics, or a related quantitative field
Demonstrated expertise in ML and LLMs, with a proven track record of applying them to real-world business problems
Strong programming skills (Python preferred), including building maintainable, production-oriented tools
Experience designing data pipelines and working with structured and unstructured datasets
Strong analytical thinking and problem-solving skills; self-directed with sound independent judgment
Excellent written and verbal communication skills, including the ability to explain technical concepts to non-technical stakeholders
Genuine interest in financial markets and motivation to apply technical skills to investment and deal execution problems

Nice To Haves

Prior experience in deal pitching, origination, underwriting, or investment proposal development
Familiarity with tax credit investments (e.g., affordable housing, renewable energy) or related structured finance products
Experience integrating analytics/AI solutions into business workflows and enterprise systems
Experience rapidly prototyping solutions and iterating based on stakeholder feedback in a front-office environment

Responsibilities

Develop a deep understanding of the end-to-end deal lifecycle (origination, underwriting, syndication, asset management) and identify opportunities to improve execution
Identify process inefficiencies and data gaps across deal workflows; design and implement ML/LLM solutions to improve speed, accuracy, and insight
Build and deploy LLM-enabled tools to automate review of legal, underwriting, and deal documentation and extract structured insights from unstructured data
Enable high-quality content generation (e.g., investment summaries, credit memos, and investment committee proposals) with appropriate controls and review workflows
Design and maintain Python-based tools and data pipelines to support market intelligence, deal sourcing insights, and scenario analysis
Deliver portfolio, pipeline, and performance reporting that supports investment decision-making and ongoing asset management
Partner with technology and internal stakeholders on integrations with origination, risk/P&L, and asset management systems; rapidly prototype and iterate based on business feedback