JD Case Study · Airbnb
A Staff-level role embedding measurement into a global payments platform — causal inference, experimentation, executive scorecards, and payment-flow optimization across a multi-sided marketplace. The experimentation + funnel + exec-narrative engine I’ve shipped is exactly this, pointed at money movement instead of merchandise.
One kind of number here. The analysis below runs on illustrative figures invented to show method. No Airbnb data is used or implied; the operating logic is the deliverable.
What they’re actually buying
Be the data thought partner for global Payments. Turn the technical reality of collection, reconciliation, and settlement into decisions leadership can actually make — find the friction in the guest/host payment journey, quantify it with causal rigor (not just dashboards), and tell the story that moves the roadmap. The job is measurement as influence.
The first 90 days
The order matters. Nobody should change a system they haven’t reconciled, and nobody should present a verdict before the numbers under it can be trusted.
Days 1–30
Trace the payment lifecycle — attempt → authorization → capture → host payout → reconciliation/settlement — for guests and hosts. Baseline the success, failure, and friction rates, and meet Payments Platform, Product, and Finance/Eng.
Days 31–60
Ship an executive scorecard and leading indicators on authorization rates, payment failures, and payout friction. Size the top friction points by lost conversion and GMV — so the roadmap argues in dollars.
Days 61–90
Design an experiment (or quasi-experiment) on a payment-flow change — smart auth retries, routing, or an alternative payment method — quantify the impact with causal inference, and deliver the readout with its roadmap recommendation.
Signature analysis · illustrative data
The highest-leverage lever: authorization rate. A few recovered points — smarter retries, better routing, the right alternative methods — flow straight into booking conversion and GMV, and it’s cleanly measurable with a holdout. That’s where I’d aim the first causal study, not at the reconciliation tail.
Requirement → proof
Each line of the posting against something already built and running on this site. Where the fit is a ramp rather than a match, the row says so.
| What Airbnb asks for | What I’ve already shipped |
|---|---|
| Causal inference & experimentation (multi-sided platform) | An A/B + incrementality readout with confidence intervals, guardrails, and a sample-size calculator. |
| Executive scorecards & OKR / business reviews | An exec growth scorecard built to surface actions and the next decision — insight, not metrics. |
| User-journey friction analysis | A full funnel diagnostic that locates the leak and sizes it in dollars. |
| Expert SQL + Python/R, ETL / data modeling | Show-the-SQL blocks (cohort LTV, channel funnel), written warehouse-ready. |
| Payments / fintech domain depth | Competitive spotlights across the pay-over-time field — Affirm, Sezzle, Dave, Progressive. |
| Monitoring the global fintech landscape & regulation | A market-intelligence briefing with a "what’s shifting" read on BNPL, CFPB, and regulatory trends. |
The same engine
The engine is the same one this whole portfolio runs on — a friction funnel, an experimentation framework with causal rigor, and an executive scorecard — and I already track the payments/fintech landscape closely enough to publish on it. Swap lease originations for payment flows and the machine is unchanged: find the leak, prove the fix, tell the story.
Payments analytics is the same discipline I’ve shipped in public — funnel, causal measurement, exec narrative — with a fintech domain I know cold. Let’s talk about your authorization rates. — Paul Brown