JD Case Study · Airbnb
Staff, Payments Advanced Analytics
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.
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.
How I’d own it — first 90 days
Days 1–30
Map the money flow
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
Build the measurement layer
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
Land a causal win
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: the payment funnel
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.
Illustrative figures to demonstrate the lens — a payment funnel with the highest-ROI, experiment-ready intervention identified. The real version runs off transaction-level data across markets and methods.
Requirement → proof
| What the JD 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.
See the engine — the Acima build →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