I bridge product, data science, sales, and customer success — not as a coordinator, but as the person who makes complex products actually land. 7+ years across ad tech, enterprise SaaS, and measurement platforms, I build the GTM systems, the operational infrastructure, and the AI-powered workflows that turn technical products into things teams can sell, clients can trust, and orgs can scale.
I'm looking for a team that takes craft seriously, moves with urgency, and believes AI should raise quality ceilings — not just throughput.
This is the core of what I do: take complex, bundled product changes — attribution model updates, measurement methodology shifts, data pipeline upgrades — and turn them into clear, compelling narratives for every audience.
End-to-End GTM Ownership
Own all measurement model update communications at my current company — translating data science methodology into client-facing messaging, CS enablement materials, and executive briefings.
Multi-Launch Coordination
Design and execute launch briefings for 3+ concurrent P0 product releases in a single week, coordinating across product, data science, engineering, CS, and sales simultaneously.
Company-Wide Enablement
Author the product fact sheets, training decks, and readiness documentation used company-wide — the canonical source of truth for how products are understood and sold.
Measurement Positioning Framework
Built the framework that standardized how the entire product portfolio is communicated across sales and CS — creating consistency and confronting the gravity of measurement myth.
Every technical product has a story. The job isn't to simplify — it's to find the frame that makes complexity feel like value.
Operations & Systems
I Build the Infrastructure Nobody Else Will
I've built operational systems from scratch multiple times — not because it was assigned, but because the gap was visible and nobody else was closing it. Each of these started the same way: I saw a structural problem, designed a solution, and shipped it — usually before anyone asked.
Client Operations Portal
A centralized portal tracking client stage, account health, measurement readiness, and value milestones for 85+ active accounts — the company's first complete post-sales client funnel view.
Automated CRM Hygiene
Auto-renewal deal creation, weekly pipeline audits validating field completeness and contract accuracy — reducing manual ops overhead by ~70%.
Data Readiness System
Covers multiple measurement products across all major ad platforms — with automated status tracking, CS talking points, and client-facing documentation built in.
Revenue Reporting Schema
The company's first revenue reporting schema, reconciling deal types across sales, finance, and CS to enable accurate recurring revenue tracking for the first time.
AI-Native Work
How I Actually Work With AI
I don't just use AI tools — I design AI-native workflows that systematize real work. The difference matters: using AI is asking it a question. Working with AI is building systems where human judgment and AI capability compound.
Communications Drafting Pipeline
Voice input → structured knowledge base → LLM-generated output → human review. Turns 3-hour writing tasks into 20-minute review cycles.
Campaign Analysis Automation
AI reads naming conventions, maps tactics to business categories, generates groupings — with human-defined constraints, not open-ended pattern matching.
Autonomous Launch Announcements
Weekly product announcements generated from structured launch logs — autonomous loop with human oversight baked in by design.
AI Coaching for Teams
Teaching the prototype-to-production handoff pattern, context engineering principles, and when to trust vs. override AI output.
Working Thesis
The quality ceiling of any AI output is set by the context you bring to it.
Prompt engineering was last year's game.
Context engineering — structuring what the model knows, when it knows it, and what constraints shape the output — is where the leverage actually lives.
Career Arc
From Buy-Side to Build-Side
A career that looks horizontal is actually a spiral — each role added a layer that the next one needed.
1
Media Buying — NYC
3 years. Learned how advertising actually works from the buy side — platform mechanics, attribution windows, what makes campaigns perform. Not abstract knowledge; the intuition that later made measurement products legible.
2
Ad Platform Operations & Product
4 years, Major Tech Platform. Progressive roles across advertising monetization operations, enterprise SaaS product marketing, and product management for data analytics and recommendation systems. Learned to work at scale, ship across functions, and translate between technical and commercial teams.
3
Measurement & AI-Powered Operations
Current. Product manager at an ad-tech measurement startup, owning incrementality testing, attribution modeling, and channel-level performance analysis across all major platforms. Built the operational and GTM infrastructure from the ground up.
This is where the buy-side intuition, platform-scale experience, and builder instinct converge.
What I'm Looking For
Balance Craft and Speed
A team where shipping means something was built well, not just built fast. Where the standard is set by what's right, not what's expedient.
AI as a Serious Capability
Integrated into how the company works — not bolted on as a feature. A place where the question isn't whether to use AI, but how to use it.
Proximity to What Matters
Close to the product, close to the customer, close to the decisions that shape how both evolve.
I'm at my best when the people around me care as much about getting it right as getting it done.