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 had three parts: financial modeling, market research, and the quiet internal data that keeps a small investment team from losing context.

I write here about the methods I used and what they taught me. Live deal terms, fundraising information, counterparty names, and relationship records stay off the site.

Modeling

I supported scenario analysis for early-stage climate and infrastructure opportunities. The difficult part was never adding another tab. It was finding the assumptions that could actually change 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
• Turned model results into concise decision materials, with ranges and assumptions left in plain sight

The work reinforced a simple lesson: an investment model should expose judgment under uncertainty, not hide it behind decimal places. A defensible range is worth more 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, who in the value chain might capture the benefit, and what evidence would prove the present view wrong? Beginning there kept the research attached to an investment decision instead of letting it become a basket of interesting facts.

Most briefs were short. Their job was to leave behind a reusable base: enough to answer one focused question quickly, and honest about the places where the answer still stood on uncertain ground.

Investment-data operations

The third part was an internal prospect-tracking workbook. I helped turn scattered records into a more consistent shared memory: common stages, clearer categories, links between people and activity, and an explicit owner and next action.

The principle was modest. A small team's data should preserve context without pretending that every relationship fits neatly into a schema. Fields make records searchable; restrained, specific notes hold what fields cannot.

A pipeline is a form of memory

Opportunities often disappear through drift rather than rejection. A pipeline is therefore partly a collective memory: it should make ownership and the next action visible, while asking the team to collect only what it genuinely needs and can responsibly maintain.

Confidentiality

This page omits deal-specific analysis, nonpublic company information, investor records, internal documents, and active fundraising details. It describes my work and its lessons; it does not reproduce the firm's data.