Transform how investments get executed by bringing AI-driven automation and analytics directly into the deal lifecycle. Partner side-by-side with senior investment professionals to build tools that accelerate decision-making and improve rigor across complex transactions.
Job summary
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.
Job 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
Required qualifications, capabilities, and skills
- 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
Preferred qualifications, capabilities, and skills
- 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