Lead Data Analyst, Growth & Experimentation
New
L
LawnStarterHome services marketplace
Workable locations: Brazil. São Paulo, State of São Paulo, Brazil. Belo Horizonte, State of Minas Gerais, Brazil. Rio de Janeiro, Rio de Janeiro, Brazil. Curitiba, Paraná, Brazil. Florianópolis, Santa Catarina, Brazil, overlap hours with US-facing partnersFull-TimeLead
Salary75,000 - 100,000 USD per year
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Job Details
- Required Skills
- PythonSQLdbt
Requirements
- Bring a point of view on experiment design, including power, significance, novelty and interaction effects, and when not to test.
- Use expert SQL and enough Python to automate statistical-significance calculations.
- Be comfortable with dbt and Lightdash and build models without waiting on data engineering.
- Understand how Segment events and Flagsmith randomization interact, and identify instrumentation problems before tests launch.
- Understand CAC, LTV, channel economics, attribution challenges, seasonality, and channel mix.
- Use AI daily for SQL, dbt, and pressure-testing analysis, and extend existing experimentation skills.
- Translate analysis into clear recommendations and challenge conclusions when the data does not support them.
- Work independently to identify what is worth measuring and partner closely with Growth.
Responsibilities
- Design Growth experiments, including power and sample-size decisions, significance standards, and readouts.
- Identify underpowered tests and false positives, and apply anytime-valid monitoring for early decisions.
- Extend automated Growth metrics in Lightdash, Python-backed statistical-significance tooling, and Claude routines.
- Build and maintain an instrumented visitor-to-lead-to-customer funnel across brands and channels.
- Analyze CAC, LTV, conversion rates, and channel economics to help prioritize Growth investment.
- Partner with Growth stakeholders to shape experiment decisions and communicate findings and uncertainty.
- Automate routine experimentation workflows so standard results can be self-serve.
- Recommend an experimentation-stack approach and develop standards and a playbook for scaling the function.
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