**About LawnStarter** 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. **About the Data Team** 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. **The Role** 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. **What makes this role 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, who come to you when a test needs a call. **Requirements** **What You'll Own** * **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. **Problems to Solve** **Tests that can't answer the question they were run for** Growth wants more experiments, but volume without rigor produces confident, wrong conclusions. Raising the bar without becoming the bottleneck is the job. **Getting off the manual treadmill** Real tooling already exists: test\-design helpers, dashboards, AI skills. Most tests are still hands\-on and bespoke. How do you extend that automation so routine cases genuinely self\-serve? **Making the funnel decision\-grade** The semantic layer defines the funnel, but instrumentation is uneven across brands and channels, and no one owns the single trusted view. You build it, and you keep it trusted. **Turning analysis into decisions** The hard part isn't the SQL. It's getting a PM or marketer to change course. Can you deliver insight sharp enough that the room acts, and push back when the data favors the popular but wrong idea? **What Success Looks Like (Year 1\)** * **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 acquisitionconversion funnel is instrumented across every brand and channel; Growth prioritizes off your model, not side spreadsheets. * **Measurable co