Work
More outreach. The same team.
I built and deployed a five-agent outreach system that finds relevant businesses and contacts, checks the research against a defined rubric, writes and sends outreach, and keeps the CRM updated. A reply monitor scans the inbox and texts us when a high-priority prospect needs a response. The system increased weekly outreach 4x without adding headcount, while keeping a person responsible for the conversations that matter.
I established the product analytics and reporting system: a North Star metric, a KPI framework, an event taxonomy, and dashboards spanning acquisition through retention. I also manage a $5K monthly paid acquisition budget, from audiences and creative tests to conversion tracking and weekly optimization.
Across user research, onboarding, email, push notifications, and channel tests, I turn what we learn into experiments aimed at activation and retention.
Agents find the fit. I make the deal.
My multi-agent system runs brand sourcing, outreach, QA, and meeting booking for creators with a combined audience of 1.1M+. It brings me high-fit brands to negotiate with personally.
The pipeline generated $60K for one creator in a single month, exceeding his previous full-year total. I also shape the campaigns, from creative briefs to competitions, livestreams, and short-form content.
A new route from a brand to a doorstep.
NiBL was a food delivery platform that put brand samples inside orders. I built the business from zero, securing 6 CPG brand partners, 100+ restaurant LOIs, and 3 office partnerships.
I ran acquisition through door-to-door outreach, flyering, paid ads, and social content. Early feedback took us from a WhatsApp MVP to a standalone app in under 2 months.
Find where people stop. Start there.
As WOM’s first growth and data insights hire, I built a campus ambassador network that brought in 500+ early users, and conducted 100+ user interviews to connect what people said with what they did in the product.
I found a payment drop-off that led to a Stripe-to-Venmo switch and a 60% increase in payment connection completion. A TikTok scraper I built compiled 5,000+ restaurant entries to guide content and growth.
Fire, Labor, and Recommitment
Does the kind of work people do in California’s fire camps relate to what happens after release? I organized 37,000+ records, built a geospatial dataset, and used linear and logistic regression models and exact matching to study fire exposure and recommitment.
Matched samples showed fewer returns to fire camp among people from camps with greater wildfire exposure, though the pattern did not hold in every regression model. The data captures returns to fire camps, not all reoffending. It can’t fully distinguish rehabilitation through purposeful work from a reluctance to return to camp.
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