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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 with rigor and own the statistical integrity.
The data team is high-leverage, embedded across the business, owning the semantic layer and metrics everyone trusts. Experimentation 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 and running. 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.
Key responsibilities:
- Experimentation rigor: test design, power and sample-size calls, significance and readout standards. Catch underpowered tests and false positives before they become bad decisions.
- Self-serve experimentation layer: automated Growth metrics in Lightdash, Python-backed stat-sig tooling, and AI skills (Claude routines) for pre-test power calcs and live-test health checks.
- Growth funnel model: a trusted, instrumented view of visitor → lead → customer across every brand and channel, with CAC, LTV, and conversion-rate cuts Growth needs to prioritize investment.
- Growth analytics function model: 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.
Success in Year 1 means: rigor is the default (power calculations standard, early stops from anytime-valid boundaries), routine tests self-serve (metrics and stat-sig automated in Lightdash), a funnel Growth trusts (instrumented across every brand and channel), and measurable conversion wins directly driven by your analysis.
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.
REQUIREMENTS:
- AI-native: use AI daily for SQL, dbt, and pressure-testing analysis, extending the skills already running the experimentation process.
- Partner, not report-writer: sit close to Growth, bring questions before asked, push back when needed.
- Statistically sharp: deep point of view on test design (power, significance, novelty and interaction effects, when not to test), can explain broken experiments in plain terms.
- Fluent in experiment instrumentation: understand how Segment events and Flagsmith randomization interact, catch tracking problems before test ships.
- Fluent in the funnel: think in CAC, LTV, and channel economics, know acquisition data's quirks (attribution messiness, seasonality, channel mix).
- Technically self-sufficient: expert SQL, enough Python to automate stat-sig math, comfort in dbt and Lightdash, build your 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.
