Senior PM who owns products end-to-end with data rigor — currently leading pricing & monetization at Brazil's largest bus marketplace.

The problem

ClickBus' low-fare product — advance purchase of carrier seat inventory resold with dynamic fares — was relevant but couldn't scale: billing was 100% manual, margin guardrails lived in spreadsheets with no real-time integration, discounts were fixed regardless of context, campaign management was fragmented, there was zero experimentation infrastructure, and search responded in ~400ms. Roughly 9% of purchased seats expired unsold.

What I did

• Shipped a unified campaign-management platform: parameterized search, 360° campaign view, one-click activation.
• Built a proposal simulator & validator that replaced an external tool, automating proposal generation and post-negotiation financial validation.
• Launched dynamic per-bus pricing ("smart discount") on an architecture designed to evolve into ML without a rewrite, and moved margin guardrails from spreadsheets to real time.

The problem

The low-fare flow historically cancelled 4–5 percentage points above the regular flow — a persistent revenue leak nobody had root-caused.

What I did

• Diagnosed the root cause with my own data analysis (Python, data lake): low-fare tickets lacked the exchange/cancellation self-service the regular flow had.
• Redesigned the exchange and cancellation flows and measured impact with a counterfactual analysis, presented at c-level review.

The problem

Leadership debated whether low fares actually created demand or just cannibalized full-price sales — with no experimental evidence either way, and no infrastructure to get it.

What I did

• Designed and ran the experiment end to end — 17 routes of a major partner carrier, 8,400+ users, deterministic bucketing, pre-defined significance criteria.
• Documented the results as a negotiation asset now used by the commercial team with carriers.

The problem

Pricing decisions across ClickBus' sales channels were made with a fragmented view — marketing, strategy, commercial, product and engineering each held a partial picture. The relationship with partner carriers lived in silos too: commercial negotiated inventory, finance fought processes case by case, and nobody owned a unified read on the B2B experience.

What I did

• Ran structured discovery across 5 internal areas (marketing, strategy, commercial, product, engineering) and 3 carrier-facing workstreams: inventory, finance, B2B experience.
• Inventory discovery fed directly into the company's largest seat-inventory deal in recent years; financial discovery surfaced gaps later formalized in the product's external audit.
• B2B-experience discovery led tribe leadership to invite me to co-design the company's B2B product, its main goal for the year.

The problem

One PM, four products: keeping the delivery bar high across discovery, specs, GTM and impact analysis doesn't scale by working more hours. I rebuilt the way the work itself gets done.

What I did

• Built a pipeline of 14 custom Claude Code skills covering the full cycle: raw data → analysis → discovery brief → PRD → prototype → spec + ADRs (with automated validation) → issues → implementation → GTM → impact analysis.
• Automated board governance with GitHub Actions and migrated squad tracking from Jira to GitHub Projects.
• Mentor PMs and engineers across the company, sharing cases to raise the whole product team's bar.