About LawnStarter

LawnStarter is the nation's leading on-demand marketplace for lawn care and outdoor services, with over $100M in annual bookings. We're expanding beyond lawn care to become the one-stop shop for all home services — and building a world-class conversion engine is our next major bet in that direction.

About the Growth Team

The Growth team owns the full customer acquisition funnel — from ad click to first completed service. We acquire 150,000+ new customers per year across paid, organic, and partner channels. Our CMO owns the visit-to-customer rate, and every point of improvement flows directly to revenue. We're building a rigorous experimentation program across the acquisition funnel, and this role is the person who makes that real.

The Challenge

We know experimentation works. We've seen tests move conversion meaningfully. We have a testing program, methodology standards, a learning repository, and a coordination layer — but no one owns it full-time. Meanwhile, we're migrating our landing pages to Webflow, which gives us full control over the pages that receive all inbound traffic — no engineering dependency, no sprint queues. The opportunity is massive, but only if there's a dedicated person behind the wheel.

The Role

You are the conversion performance engine for LawnStarter. You own the landing pages in Webflow, build and run A/B tests in VWO, and use AI, Tableau and Hotjar to find the next thing to test. You report to the CMO and are the primary executor moving the visit-to-customer rate — the metric that matters most for growth.

This is not a strategy-and-slides role. You build variations yourself, configure goals yourself, debug tracking yourself, and ship yourself. You have no engineering support and you don't need it. Your Webflow and VWO skills are production-grade.

Today this is a solo operator role. The expectation is that you build the program, prove the value, and design the function that scales it.

What makes this role different:

• You own the full stack — pages, tests, tracking, analysis — with zero engineering dependency
• We have a conversion testing program and piping in place — you're the first dedicated owner to scale it into a rigorous experimentation function
• AI is embedded in your workflow — hypothesis generation, analysis, variation content — not a nice-to-have

What You'll Own

• Conversion performance — visitor-to-lead (landing pages you control directly) and lead-to-customer (booking funnel you optimize via testing). These two levers compound into the visit-to-customer rate.
• Webflow and landing page architecture — paid landing pages, thousands of organic city/near-me pages across all brands and services, and the CMS structure that makes them scalable without engineering. You own conversion optimization across the entire landing page surface area.
• The experimentation program — a multi-quarter testing roadmap for the acquisition funnel, from ad click to first completed service. You build the methodology, run the tests, and feed learnings back into the next cycle.
• AI-accelerated testing workflow — use AI for hypothesis generation from behavioral data, result summaries within 24 hours of test completion, and variation copy/design briefs that increase test throughput.
• The experimentation function model — by end of Year 1, you've documented the team structure, tooling stack, and processes needed to scale from solo operator to a repeatable function.

Problems to Solve

Moving the visit-to-customer rate Two isolated levers compound into one metric: visitor-to-lead (landing page conversion you own directly) and lead-to-customer (booking funnel conversion you optimize via testing). Both have room to grow, both swing seasonally, and improving one without the other leaves value on the table. How do you move both simultaneously across all channels and brands?

Capitalizing on the Webflow migration For the first time, marketing controls production pages — no engineering dependency, no sprint queues. Paid search landers, thousands of organic city and near-me pages, partner pages, referral pages, home pages, paid social landers — all moving to Webflow, all needing conversion optimization. How do you build scalable CMS architecture, improve Core Web Vitals, and start running high-velocity tests the moment you have the keys?

Embedding AI into every stage of the workflow We don't want AI as a side project. Hypothesis generation from session data and heatmaps. Result readouts compressed from days to hours. Variation copy drafted before the designer opens Figma. How do you build an AI-native experimentation workflow that makes manual-only approaches feel slow by comparison?

Who You Are

Lives in Webflow. You spend most of your time building pages, not reading reports. Webflow is your production environment, not a design tool. You find friction through screen recordings, customer calls, and sales emails — then you ship the fix yourself.

Statistically sharp. We currently use two-tailed p-tests and build our own Tableau dashboards to call tests. You should have a point of view on when that's the right approach and when it's not. One of your first contributions will be documenting methodology standards for the team.

AI-native. You use AI for hypothesis generation, variation copy, and analysis acceleration — every day, not occasionally. If you describe AI as something you're "exploring," this isn't the role.

Ships fast. You measure weeks in tests launched and learnings captured, not decks delivered. You'd rather run the test and learn than plan it perfectly.

Gets the stats right. You catch underpowered tests, peeking, and interaction effects. You know when NOT to test. You can explain results to a non-technical stakeholder without losing them.

This Role Is NOT

• A strategy role. You will set strategy, but you also build the pages, configure the tests, and pull the data. If you want to think about experimentation without doing it, this isn't the role.
• A people management role (yet). This starts as a solo operator. You'll design the function and make the case for headcount, but Year 1 is about proving the model yourself.
• An engineering-supported role. There is no frontend dev building your variations. There is no data engineer setting up your tracking. You are the implementation layer.

• Base salary: $160,000-$180,000
• Equity: Every point of conversion improvement flows directly to revenue across all brands. We want you invested in the long-term outcome of the experimentation program you're building.
• Healthcare: Medical, dental, and vision
• Fully remote: Work from anywhere in the US
• Unlimited PTO: Focused on outcomes

LawnStarter provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, or genetics. We comply with applicable state and local laws governing nondiscrimination in employment.