The Tax Oriented Investments team is seeking a highly analytical and technically strong associate to apply machine learning (ML) and large language models (LLMs) to front‑office investment workflows. This role partners closely with investment professionals to modernize deal origination, underwriting, and asset management through automation, analytics, and AI‑enabled decision support.
Job Summary:
As an Associate on the Quantitative Trading & Research (QTR) Team, you will sit at the intersection of quantitative research, AI engineering, and front-office deal execution, offering direct exposure to senior investment professionals on complex, large-scale transactions. You will be instrumental in modernizing the deal flow through intelligent automation, data-driven insights, and the deployment of AI solutions.
**Job Requirements: **
Develop a deep understanding of the end-to-end deal lifecycle (origination, underwriting, syndication, asset management)
Identify process inefficiencies and data gaps across deal workflows; design and implement ML / LLM‑based solutions to improve speed, accuracy, and insight
Build and deploy LLM-enabled tools to:
- Automate review of legal, underwriting, and deal documentation
- Extract structured insights from unstructured deal data
- Generate investment summaries, credit memos, and investment committee proposals
- Support scenario analysis and decision‑making through intelligent analytics
Design and maintain Python‑based tools and data pipelines
- Market intelligence and deal sourcing insights
Portfolio, pipeline, and performance reporting
Partner with technology and internal stakeholders on integrations with originations, risk and P&L, and asset management systems
Rapidly prototype and iterate solutions in response to 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 LLM models, with a proven track record of applying to real-world business problems
- Strong programming skills (Python preferred)
- Excellent written and verbal communication skills, with the ability to translate complex technical concepts for non-technical stakeholders
- Self-directed with strong critical and independent thinking abilities
- Genuine interest in financial markets and drive to apply technical skills to financial problems
Preferred qualifications, capabilities, and skills:
- Prior experience in deal pitching, origination, underwriting and investment proposals
- Familiarity with tax credit investments (e.g., affordable housing, renewable energy) or related structured finance products