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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. The company runs a rigorous experimentation program with pre-registered analysis plans, anytime-valid statistics, automated health checks, and AI-driven test lifecycle management.
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 across paid, organic, and partner channels. The CRO and Growth PMs decide what to test and direction; you shape the rigor and own the analysis.
Key responsibilities:
- Experimentation rigor: test design, power and sample-size calculations, significance standards, and readout protocols. You catch underpowered tests and false positives before they become decisions, and move Growth's tests onto the anytime-valid monitoring already in place.
- Self-serve experimentation layer: extend automated Growth metrics in Lightdash, Python-backed statistical-significance tooling, and AI skills (Claude routines) for pre-test power calculations and live-test health checks.
- Growth funnel model: build and maintain a trusted, instrumented view of visitor → lead → customer across every brand and channel, with CAC, LTV, and conversion-rate cuts for investment prioritization.
- Growth analytics function model: by end of Year 1, establish standards and playbooks to scale this function beyond one person, plus a buy-vs-build recommendation for the experimentation stack.
Year 1 success metrics: rigor becomes default (power calculations standard, early stops from anytime-valid boundaries, reduced re-run rate); routine tests self-serve (metrics and stat-sig automated in Lightdash); a funnel Growth trusts (instrumented across all brands and channels); measurable conversion wins directly driven by your analysis.
You are expected to be AI-native, using AI daily for SQL, dbt, and analysis pressure-testing. You partner directly with Growth, bringing questions proactively rather than waiting for tickets. You are statistically sharp with strong opinions on test design, power, significance, and when not to test. You are fluent in experiment instrumentation (Segment events, Flagsmith randomization), the funnel (CAC, LTV, channel economics, attribution messiness, seasonality), and technically self-sufficient (expert SQL, Python for automation, dbt, Lightdash). You influence without authority: PMs and marketers act on your insights because they are clear and honest about uncertainty.
This role is NOT a ticket queue, NOT the person who decides what to test (you shape rigor, they decide direction), NOT a product-feature analyst seat, NOT a pure platform/dashboard-builder role, and NOT a people-management role. It is an individual-contributor seat with deep analytical scope and organizational weight—the CEO personally engages with test design here.
Requirements:
- AI-native: daily use of AI for SQL, dbt, and analysis; comfort extending existing Claude routines rather than skepticism of AI integration.
- Statistical expertise: strong point of view on test design (power, significance, novelty and interaction effects, when not to test); ability to explain broken experiments in plain terms; understanding that "we hit significance" is the start, not the end, of analysis.
- Experiment instrumentation fluency: knowledge of how Segment events and Flagsmith randomization interact; ability to catch tracking problems before test launch.
- Funnel fluency: thinking in CAC, LTV, and channel economics; understanding acquisition data quirks (attribution messiness, seasonality, channel mix); not pure product-feature analytics background.
- Technical self-sufficiency: expert SQL, Python for statistical-significance automation, comfort with dbt and Lightdash; ability to build models without waiting on data engineering.
- Partnership mindset: proactive engagement with Growth team; ability to push back when data favors the unpopular but correct idea; focus on driving decisions, not just shipping analysis.
- No requirement for people management or prior leadership experience.
