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Salary: USD 75,000 - 100,000 / annual
LawnStarter is the nation's leading on-demand marketplace for lawn care and outdoor services, with over $150M in annual bookings, expanding into a one-stop shop for all home services.
You are the first data analyst dedicated entirely to Growth and Experimentation. Your primary charter is the experimentation program: test design, statistical rigor, and readouts across web funnels, SMS/drip, sales-driven tests, and SEO tests. You also own the acquisition-to-conversion funnel those tests move, across paid, organic, and partner channels. The CRO and Growth PMs decide what to test and direction; you shape it and own the rigor.
The data team is high-leverage, embedded across the business, owning the semantic layer and trusted metrics. The experimentation program runs on real rigor: pre-registered analysis plans gate every test launch, anytime-valid statistics keep dashboards honest under continuous viewing, automated daily SRM and attribution health sweeps catch broken tests early, and seasonal power forecasting accounts for business seasonality. The test lifecycle is already AI-driven.
You're not starting from scratch. Dashboards, tooling, and rigor scaffolding are already shipped. Early months will be hands-on and manual: scoping tests, crunching readouts, while you build toward a self-serve layer. If a test readout and funnel refresh compete for your week, the test wins.
What makes this different: the CEO personally engages with test design here—real organizational weight, no fight for buy-in. You partner directly with the Director of CRO, performance marketing, and Growth PMs.
You will own: experimentation rigor (test design, power and sample-size calls, significance and readout standards); the self-serve experimentation layer (automated Growth metrics in Lightdash, Python-backed stat-sig tooling, Claude routines for pre-test power calcs and live-test health checks); the Growth funnel model (a trusted, instrumented view of visitor → lead → customer across every brand and channel, with CAC, LTV, and conversion-rate cuts); and by end of Year 1, the standards and playbook that scale this function beyond one person, plus a buy-vs-build recommendation for the experimentation stack.
Problems to solve: tests that can't answer the question they were run for (raising the bar without becoming the bottleneck); getting off the manual treadmill (extending automation so routine cases genuinely self-serve); making the funnel decision-grade (building a single trusted view across uneven instrumentation); turning analysis into decisions (delivering insight sharp enough that the room acts).
Year 1 success: rigor is the default (power calculations standard, early stops from anytime-valid boundary, re-run rate down); routine tests self-serve (metrics and stat-sig automated in Lightdash); a funnel Growth trusts (instrumented across every brand and channel); measurable conversion wins (analysis directly drove specific, quantified lifts).
REQUIREMENTS:
- AI-native: use AI daily for SQL, dbt, and pressure-testing analysis, extending skills already running the experimentation process. Not a good fit if skeptical of AI or treating workflow as fixed.
- A partner, not a report-writer: sit close to Growth, bring questions before asked, push back when needed. Not for those wanting a clear queue with no expectation to push back.
- Statistically sharp: wrong fit if "we hit significance" ends analysis instead of starting it. Right fit with point of view on test design (power, significance, novelty and interaction effects, when not to test) and ability to explain broken experiments in plain terms.
- Fluent in experiment instrumentation: know how Segment events and Flagsmith randomization interact, catch tracking problems before test ships. Not for you if instrumentation is someone else's job.
- Fluent in the funnel: think in CAC, LTV, and channel economics, know acquisition data's quirks (attribution messiness, seasonality, channel mix). Skip if background is pure product-feature analytics.
- Technically self-sufficient: wrong fit if you need clean data handed to you. Expert SQL, enough Python to automate stat-sig math, comfort in dbt and Lightdash, building own models without waiting on data engineering.
- Influences without authority: PMs and marketers act on your findings because insight is clear and honest about uncertainty. Not for you if job is done once analysis ships, regardless of outcome.
