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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 and two consecutive years of profitability. The company is expanding beyond lawn care to become a one-stop shop for all home services and is investing in the next generation of its platform.
You'll join a high-leverage Product Data Analytics team embedded across the business, owning the semantic layer and the metrics everyone trusts. As a Lead Product Data Analyst, you'll directly impact business results through insights and reports, working closely with product managers, researchers, and business stakeholders on prioritization, assessments, and business recommendations.
This is an individual-contributor role where "Lead" describes the bar you hold yourself and others to, not a team you manage. You'll work with autonomy, setting your own standards, working independently, and acting as a trusted thought partner rather than an order-taker. The role leans toward the Pro (supply) side of the marketplace, though focus flexes with business needs.
Key responsibilities include:
**Modeling & Analysis:** Navigate a complex marketplace system with many moving parts and often contradicting signals. Conduct analyses ranging from simple A/B tests to multivariate models on retention or ETA, backed by advanced SQL and intermediate Python or R.
**Reporting:** Understand team needs and create/maintain the reporting system, keeping it organized and easy to act on. Build trust in metrics by ensuring dashboards are correct and current.
**Analytics Engineering:** Work in the Data Warehouse to provide clean, documented datasets powering reports and end users. Use dbt for transformation (SQL is a must).
You'll solve critical problems: untangling metric inconsistencies so the business has one trusted number; ensuring analysis actually changes decisions, not just gets delivered; building a data-driven culture where stakeholders can self-serve answers; and teasing out what's actually driving metrics versus noise in a complex system.
Success in Year 1 means: metrics teams rely on daily are trusted and documented; routine questions are self-serve; you can name specific decisions your analysis changed; and datasets/models you've built are clean enough for others to build on.
You'll use SQL, dbt, Python or R, and Lightdash as the primary BI layer. The company emphasizes AI-native workflows—you'll use Claude, ChatGPT, Copilot daily to move faster on SQL, dbt models, scripting, and reporting.
**Requirements:**
- Advanced SQL proficiency and experience with dbt for data transformation and modeling
- Intermediate Python or R for statistical analysis and modeling
- Demonstrated ability to conduct complex analyses (A/B testing, multivariate modeling, retention analysis)
- Experience building and maintaining dashboards and self-serve reporting systems
- Strong business acumen and ability to connect analysis to product, customer, and financial outcomes
- Comfort working autonomously and setting high analytical standards
- Daily use of AI tools (Claude, ChatGPT, Copilot) to accelerate work
- Learning mindset with ability to understand problems deeply before reaching for solutions
- Bias for action and comfort working with constraints; ability to avoid perfectionism
- Track record of analyses that drove actual business decisions, not just delivery of reports
- Experience in marketplace, supply-demand, or complex multi-sided platform environments is a plus
- English resume required for application consideration
