Fiscal Integrity
Scaling Forecast Accuracy from 70% to 95%
A "lumpy" pipeline with no discernible patterns made revenue forecasting nearly impossible, creating friction between Sales and Finance.
With two decades of experience scaling SaaS leaders from Series A to IPO, I specialize in the architecture of high-growth revenue engines. I align Finance, Sales, and Product through data-driven strategy and AI-enhanced workflows to ensure organizations stay agile in an evolving market.
Operating Philosophy
I build the “single source of truth” environments where data-driven strategy replaces reactive decision-making—transforming operational debt into a scalable engine for growth.
Predictability at Scale
I partner with CFOs to translate complex pipeline data into high-fidelity forecasting and board-ready reporting.
Frictionless GTM Alignment
I design infrastructure where PLG and Sales-Led motions work in tandem, mirroring the modern customer journey from trial to renewal.
AI-Enhanced Productivity
I leverage automation and AI workflows to slash AE ramp time and accelerate ‘Time to First Deal,’ allowing people to move at peak speed.
Expertise
Partnering with the CRO and CFO to translate pipeline activity into board-credible forecasts and predictable revenue.
Designing the territory, segmentation, and motion that turn market opportunity into repeatable pipeline.
Connecting product signals — usage, activation, and expansion — to the revenue motion so PLG and Sales work in tandem.
Lead-to-cash design, deal desk, forecasting cadence, and the operating rhythm that holds the GTM engine together.
Transforming raw data into actionable insights through AI-driven forecasting and pipeline health analytics.
Establishing the rules, data hygiene, and validation logic that keep the engine clean and the numbers trustworthy.
Case Studies
Three representative engagements where strategy met systems — and the numbers moved.
Fiscal Integrity
A "lumpy" pipeline with no discernible patterns made revenue forecasting nearly impossible, creating friction between Sales and Finance.
Architectural Transformation
Rapidly doubling field headcount led to a slow, manual quote-to-cash process where historical customer data lived in disconnected Google Drives.
Human Velocity
Scaling the sales org was "scaling the chaos," with inconsistent territory assignments and resistance to new forecasting tools.
Every result above was achieved by aligning technical architecture with cross-functional human strategy.
Experience
Currently exploring Revenue and Sales Operations leadership opportunities at growth-stage SaaS companies.
runZero
Remote
D2iQ
Remote
Visier
Hybrid. Sunnyvale, CA and Vancouver, BC
ThoughtSpot
Palo Alto, CA
Castlight Health
San Francisco, CA
Selected Projects
I designed and shipped this site using Claude (Anthropic) and Lovable, an AI-native web builder. No agency, no developer, no months-long project. I wrote the strategy, drafted the copy, and iterated on the design through prompts, treating the AI as a junior team member I was directing. It's live and it took days not months. And I will continue to iterate and release as I work on it. It's also a live demonstration of how I approach any new operational tool: start with the outcome, prompt toward it, and iterate until it's right.
What this shows
That's how I approach operational tooling: find the right leverage, move fast, own the output.
I've replaced manual overhead with AI workflows across my day-to-day: Granola captures meeting notes automatically so nothing falls through the cracks. Whispr Flow handles transcription and action extraction. Claude handles analysis and GTM decision-making. Gong surfaces deal risk without manual review. The result: the cognitive overhead of tracking everything is largely gone. I spend time on judgment calls, not information management.
What this shows
The best RevOps leaders today aren't just operators. They're systems designers who use AI to multiply their own output.
I built automated pipeline inspection workflows that ran before every weekly forecast call, flagging deals at risk of slipping based on activity signals, stage age, and close date movement. Instead of waiting for a rep to self-report a problem on Thursday, the system surfaced it on Monday. The result: fewer surprises, more productive forecast calls, and a culture shift from reactive firefighting to proactive deal management.
What this shows
Forecast accuracy isn't a one-time fix. It's an ongoing system. I build the infrastructure that keeps it honest.
Static lead scoring was sending SDRs after the wrong accounts — high firmographic scores, low actual buying intent. I replaced it with an AI-assisted model that combined firmographic data with behavioral signals: product usage patterns, content engagement, and historical conversion data by segment. SDRs stopped chasing cold leads and started working accounts already showing intent signals. Within 90 days, connect rates improved by 30%+, and time-to-pipeline dropped meaningfully.
What this shows
AI is most powerful when it removes the guesswork from high-volume, high-stakes decisions — like which accounts a seller should call today.
Skills
Leadership & Strategy
Tech Stack
This list is not exhaustive — I’m continuously adding tools and capabilities as the landscape evolves.
Let’s talk
I’m currently available for full-time, fractional, and consulting engagements. If you’re scaling a GTM motion and need an operator who’s done it before — let’s talk.