Analytics that changes what the business does, not just what it knows.
At Atlassian, a global partner ecosystem was being measured like a resale channel. At HubSpot, a 500k+ MAU ecosystem had no shared definition of success: five product teams, each optimizing for activity. In both cases I rebuilt the measurement at the layer where value actually flows, and helped the orgs change how they evaluated teams, set incentives, and invested.
- Targeting
- Director · GTM & Product Analytics · Strategy & Operations
- Based in
- NYC Metro
- Background
- Analytics · Data Science · Product Strategy · GTM & Ecosystem Strategy
- Focus areas
- Ecosystem Analytics · Partner Attribution · Business Planning · XFN Alignment · Team Leadership
Partner contribution was invisible. The ecosystem was measured like a resale channel even as direct sales and success teams grew around it.
Five product teams shared a 500k+ MAU ecosystem with no shared definition of success. Each optimized for its own activity metrics.
The integration marketplace had no personalization. Every user saw the same generic catalog regardless of use case, stack, or behavior.
HubSpot's integrations had no empirical view of their own coverage. Breadth-vs-depth investment decisions were being made on instinct, and leaders were misaligned.
IBM Software had 20,000 sellers mapped to accounts by gut instinct.
Red Hat was acquired with an assumption that an open-source marketplace would become a major distribution channel. That idea was untested.
My first project at IBM was redefining sales coverage for about 20,000 sellers. The room was deep in the mechanics, which customers belonged in which segment, and nobody had asked what we were actually trying to achieve. Opening with that question changed the path: a clear outcome, a strategy to get there, and implementation plans the data actually supported. I've been chasing that unlock since.
That's the gap I keep finding: organizations build measurement for the layer they started at, then the business changes and the measurement doesn't. At IBM it was go-to-market design and marketplace economics. At HubSpot it was what ecosystem success actually means when five product teams share 500k users. At Atlassian it was what partner contribution looks like when you stop counting partner activity solely on transactions.
Along the way: Chief of Staff to an IBM SVP running a $2B P&L, took Red Hat Marketplace from acquisition thesis to GA product, and built strategic measurement infrastructure inside three separate analytics functions.
The work I'm best at sits at the intersection of measurement, strategy, and executive alignment. Not just building the model: getting the org to optimize for the right thing.
- Technical skills
- Python · SQL · Looker · Snowflake · Databricks · Amplitude
- Outside work
- Eagle Scout. Running a Cub Scout pack in Westchester.
- Education
- MBA · Kellogg, Northwestern
- MS + BS · Electrical Engineering, Northwestern
AI I've built.
Building AI tools is part of how I run an analytics function, not a side interest. Some are internal: agents I shipped into Atlassian's partner org to cut time-to-answer and stop pipeline leakage. Others are public experiments that apply the same analytical thinking to strategy problems.
Atlassian Partner Support Bot
Internal chatbot built for Atlassian's partner team. Triages technical and business questions - data availability, trend interpretation, dashboard issues - without human intervention. Reduced time-to-answer on routine questions and freed up strategic analytics capacity.
Deal Registration Agent
Agentic workflow that monitors incoming deal registrations and proactively engages the partner sales team to get registrations reviewed and actioned before expiry. Eliminated patchwork of manual tracking processes that were a consistent source of pipeline leakage.
Arbor
Passion project. Better employee feedback tooling, starting with software developers, a profession with enough structured public data (PRs, reviews, deployment history) to make AI-assisted feedback specific and actionable rather than generic.
WSIAI: What Should I AI?
Individual-level AI strategy tool. Given your company and job title, it maps where your time is going, brainstorms specific buildable agents across six categories, and returns a prioritized playbook: not 'use AI more,' but a concrete agent specification.
Applied AI Strategy Toolkit
A connected set of agents that put a strategist's lens on a business: where customer value breaks down, where margin leaks, why a built agent isn't getting used, and what that agent is actually worth. Each runs on agent.ai.
Let's talk about where your measurement or operating cadence isn't matching how the business actually works.