• Lead and manage a robust team of sales professionals, driving sales strategy and targets • Cultivate relationships with key accounts and expand Gourmia's reach • Mentor and manage the sales team, setting clear goals • Maintain and strengthen existing relationships with major retailers • Continuously identify and pursue new sales opportunities • Attend high-stakes meetings with key clients • Collaborate with department leaders to synchronize strategies • Analyze sales data, market trends, and competitor strategies • Create detailed sales reports for the executive team

• Oversee the planning and execution of all projects within the team, ensuring adherence to project governance processes • Support and guide Study Coordinators, enabling them to achieve success and deliver consistent results • Implement project management tools and templates across the team to increase operational efficiency • Continuously monitor project portfolios for risks, deviations, and resource utilization, driving actions for improvement • Lead the planning, execution, and delivery of critical strategic projects, ensuring timely and high-quality outcomes • Serve as the primary point of contact for clients and stakeholders, fostering strong relationships and effective communication • Identify risks, create mitigation plans, and ensure proactive issue resolution to keep projects on track • Develops and manages the study budget • Overall responsibility for negotiation of financial aspects of clinical performance studies/program • Interacts with senior internal management and external personnel on significant matters often requiring coordination between involved team members and stakeholders

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?

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?