Sustainable Finance · Silver Leaf Partners

In summer 2025, I interned with the investment team at Silver Leaf Partners, a New York firm focused on climate-transition companies and infrastructure. My work fell into three connected areas: financial modeling, market research, and investment-data operations.

This is intentionally a portfolio-level account. It describes the analytical methods I practiced without reproducing live deal terms, fundraising information, counterparty names, or relationship records.

Modeling

I supported scenario analysis for early-stage climate and infrastructure opportunities. The useful part was not adding more tabs. It was identifying which assumptions actually changed the decision.

• Built and reviewed DCF and project-finance analyses under base, upside, and downside cases
• Connected operating assumptions to valuation, coverage, and return outputs so that a change in one driver flowed through the full model
• Used sensitivity tables to separate consequential inputs from decorative precision
• Translated model results into concise decision materials, with ranges and assumptions made explicit

The work reinforced a simple lesson: an investment model should expose judgment under uncertainty, not conceal it behind decimal places. A narrow, defensible range is more useful than a precise answer whose assumptions cannot survive a second reading.

Market research

I also used public databases, company materials, specialist newsletters, and industry reporting to study climate-transition markets. The recurring themes included carbon removal, transition-related environmental markets, grid and storage infrastructure, and adjacent climate services.

Each brief began with a decision question: what is changing in this market, which part of the value chain captures it, and what evidence would make the thesis wrong? That framing kept market summaries connected to underwriting instead of turning them into collections of interesting facts.

Most briefs were short. Their value came from creating a reusable research base that could answer a focused question quickly and show where the answer remained uncertain.

Investment-data operations

The third workstream was improving an internal prospect-tracking workbook. I helped turn unstructured records into a more consistent operating system by introducing common stages, clearer categorization, links between contacts and relevant activity, and explicit next steps.

The design principle was modest: a small team's data system should preserve context without pretending every relationship fits neatly into a schema. Structured fields make records searchable; carefully limited notes preserve the context that fields cannot carry.

What it taught me about pipelines

A pipeline is partly a memory system. Opportunities often disappear through drift rather than a clear rejection. A useful workflow makes ownership and the next action visible, while collecting only the information the team needs and can maintain responsibly.

Confidentiality

This page omits deal-specific analysis, nonpublic company information, investor records, internal documents, and active fundraising details. It is a description of skills and working principles, not a reproduction of the firm's data.