Founders Office - Finance - Underwriting & Intelligence Engine

Avacasa — Founders Office - Strategic Finance Intern (Underwriting Engine) JD (v1)

Location: Bangalore or Remote OK.

Employment Type: Full Time.

About Avacasa

Avacasa is an AI-native marketplace for second-home and vacation-home investment across India and neighboring markets. Alongside the marketplace, we structure fractional investment opportunities — time-bound deals where buyers co-invest in specific projects underwritten against real re-rating events.

We are building our Underwriting + Intelligence Engine from scratch — a codified system that identifies which markets are worth investing in, which projects within those markets are the right deals, and what each opportunity is actually worth. The engine serves two surfaces: Avacasa's own investment decisions, and buyer-facing valuations on our property pages.

About This Role

You will work directly with the CEO on building this engine from day 1. This is not a model-maintenance role or a reporting role. You will be in the room, in the data, and in the model, while the engine takes shape.

Specifically, you will:

Audit the data we already have (scraper outputs, developer CRM, past deal underwriting) — tell us honestly what's clean, what's garbage, and what's missing

Reproduce our live deal underwriting (Karjat, Himachal pilots) from first principles — the engine's logic has to match our ground-truth before we trust it on new deals

Draft the first market-intelligence rankings — which micro-markets are investment-worthy, and why

Build financial models (DCF, IRR, waterfalls, sensitivity analysis) under supervision; iterate them as the engine logic improves

Document everything — methodology, assumptions, data lineage — so the engine is defensible to investors, buyers, and regulators

The CEO is the senior finance brain on this project; you are the hands. If you want to learn how a real underwriting engine gets built from scratch — and have it on your CV before you turn 25 — this is the role.

Required

CFA Level 1 cleared (minimum). CFA Level 2 cleared or in progress is a plus.

Finance / economics / commerce undergrad — in final year, or a recent graduate (within 12 months)

Strong Excel + Google Sheets modeling. You can build a DCF, a waterfall, and a sensitivity table from scratch without a template.

Some coding comfort — SQL, Python, or willingness to learn. Even one Jupyter notebook from a side project counts.

Honest, evidence-first communication — you say "I don't know" when you don't; you don't hide weak assumptions behind formatting

Comfort with remote work and async feedback

Preferred

Prior internship at a PE firm, investment bank (M&A or sector-focused), family office, or wealth-management advisory

Real-estate exposure — a project, an internship, a side interest in Indian residential or infra

Familiarity with LLM tools applied to finance (ChatGPT, Claude, Perplexity for research; Cursor or similar for scripting)

Prior experience building any kind of data-driven ranking, scoring, or valuation model — academic, internship, or personal project

Not a Fit

MBA graduates expecting a managerial role. This is a hands-on build role.

CA students without finance-modeling experience. We need modeling, not accounting.

Excel-only candidates with no interest in learning any code. The engine will not live in Excel long-term.

Compensation

Stipend: ₹10,000 – ₹15,000 per month, based on CFA progress and modeling portfolio

Full-time conversion: real possibility if the engine ships and the fit is strong — the first senior finance role on the team opens up as the engine matures

Learning: direct exposure to real deal underwriting, real data, and the full build of an AI-native finance product — not a case-study simulation

Hiring Process

Apply — Apply here

Assignment — a 3–4 hour case: build an IRR + waterfall model for a hypothetical plot we specify, with a 1-page memo on how you would systematize this model to apply across many deals.

CEO call (30 min) — Praveen.

Working session (60 min) — walk through your model with Praveen, defend assumptions.

Offer.