Systems at scale, across the whole revenue function.
Built out the foundation of Databricks' Partner Strategy & Ops team: forecasting, attribution, incentives, quotas, planning — and AI deployed on top of all of it.
The operating spine of a GTM org — zero to one, then one to 100. I run the forecast, attribution, incentive, and quota systems a partner business depends on, and align them across a two-sided marketplace.
This page runs its own eval suite: every answer it gives is pinned by tests. Repo →
Systems that didn't exist before — built zero to one, scaled one to 100, and run across a two-sided marketplace: the partner team, the sellers who co-sell through partners, and the partners themselves.
Gave the partner business a number it could plan against. Still the number the org runs on today.
Settled how partner revenue gets counted: one standard the whole marketplace aligns to, still in force.
Turned partner incentives into a growth lever for new business. Still running.
Unblocked the partner channel as a revenue source: lead flow that moves in days, not weeks.
Put partner reporting on autopilot: agents that keep the org current without anyone pulling a report.
Points sellers to the right partner for the deal in front of them, in plain language.
Made the data worth building on: one foundation, shaped with stakeholders, that every agent and insight runs from.
The discipline, demonstrated from scratch: AI authors the rules, tested code owns every dollar.
Stood up at Databricks partner ops and still running: reporting, partner recommendations, and natural-language answers, from raw GTM data to agents reps actually use.
The operating rule, everywhere: the money path never touches a model. Plain-English rules → deterministic, tested code → evals pinning the math.
This repo demonstrates the same discipline, built from scratch on synthetic data: five layers, from governed data to a loop that runs the QBR on the tooling itself.
This isn't a toy domain: incentive crediting is a class of problem I know from running it — and everything here is built from scratch on synthetic data. The rule that makes AI safe in production: the model authors rules in plain English; deterministic, tested code computes anything that touches money. An eval suite pins the math — mid-quarter hires, territory handoffs, split credit, coverage gaps — so no model ever computes a credited dollar, and no deal is silently zeroed.
One résumé, the whole record. The ATS-safe version is single-column for application portals; the default is designed for reading.
The stories behind these systems are better told live.