Lead Data Analyst, Growth & Experimentation

LawnStarter is the nation's leading on-demand marketplace for lawn care and outdoor services, with over $150M in annual bookings. We're expanding beyond lawn care into the one-stop shop for all home services. Getting there depends on how fast we can test, learn, and scale what works.

We're a high-leverage team of Product Data Analysts embedded across the business, owning the semantic layer and the metrics everyone trusts. The experimentation program runs on real rigor, not vibes: pre-registered analysis plans gate every test launch, anytime-valid statistics keep mid-run dashboards honest under continuous viewing, automated daily SRM and attribution health sweeps catch broken tests early, and seasonal power forecasting accounts for a business that swings hard by time of year. The test lifecycle, design through readout, is already AI-driven. Our analysts are stretched across product, so Growth support has stayed part-time and reactive, until now.

You're 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 built on our own page-clustering tooling. It's a wider surface than most companies run. You also own the acquisition-to-conversion funnel those tests move, across paid, organic, and partner channels. What to test and which direction to bet on is the CRO's and Growth PMs' call; you shape it, they decide it.

You're not starting from scratch. Dashboards, tooling, and rigor scaffolding are already shipped and running. Expect the early months to be hands-on and manual: scoping tests, crunching readouts, while you build toward a self-serve layer. If a test readout and a funnel refresh ever compete for your week, the test wins.

• 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, who come to you when a test needs a call.

• Experimentation rigor: test design, power and sample-size calls, significance and readout standards. Core of the role: you catch underpowered tests and false positives before they become bad decisions, and you get Growth's tests onto the anytime-valid monitoring the program already runs, so early calls come from a crossed boundary instead of a hopeful trend read.
• The self-serve experimentation layer: automated Growth metrics in Lightdash, Python-backed stat-sig tooling, and the AI skills (Claude routines) already handling pre-test power calcs and live-test health checks. You extend these and keep the layer correct as product and tracking evolve.
• The Growth funnel model: a trusted, instrumented view of visitor → lead → customer across every brand and channel, with the CAC, LTV, and conversion-rate cuts Growth needs to prioritize investment.
• The 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 (an off-the-shelf stats engine, or extending our own skills and Python). You bring the recommendation; the final call isn't yours alone.

• Rigor is the default. Power calculations are standard practice, early stops on Growth tests come from the anytime-valid boundary rather than a trend read, and the re-run rate (redone for tracking or attribution problems) is down.
• Routine tests self-serve. Metrics and stat-sig are automated in Lightdash, so the team reads standard results without filing a ticket, and your time goes to the tests that need an analyst.
• A funnel Growth trusts. The acquisition→conversion funnel is instrumented across every brand and channel; Growth prioritizes off your model, not side spreadsheets.
• Measurable conversion wins. Your analysis directly drove specific, quantified lifts in the funnel, and you can name them.

• →A ticket queue. Not the role if you want well-defined asks: no one hands you a backlog of number-pulls, you decide what's worth measuring.
• →The person who decides what to test. You shape that call and own its rigor, but the roadmap and final direction belong to the CRO and Growth PMs.
• →A product-feature analyst seat. Existing analysts already own product, pricing, and service delivery; this one lives in Growth: acquisition, conversion, experimentation.
• →A pure platform or dashboard-builder. You'll automate metrics, stat-sig, and AI skills into a self-serve layer, but that's the means. The job is designing experiments and keeping that automation correct as product and tracking evolve.
• →A people-management role. This is an individual-contributor seat.