Lead Data Analyst, Growth & Experimentation

Accountabilities: Lead the experimentation program across web funnels, SMS and email campaigns, sales-led initiatives, and SEO experiments. Define experiment design standards, including statistical power, sample sizes, significance thresholds, and analysis plans. Identify underpowered tests, false positives, tracking issues, and attribution problems before they lead to inaccurate business decisions. Apply anytime-valid statistical monitoring to support reliable decisions during ongoing experiments. Develop and improve self-service experimentation capabilities, including automated Growth metrics in Lightdash, Python-based statistical significance tools, and AI-powered workflows. Maintain and extend existing AI routines supporting pre-test power calculations and live-test health monitoring. Build and maintain a trusted acquisition-to-conversion funnel covering visitors, leads, and customers across brands and channels. Analyze customer acquisition cost (CAC), lifetime value (LTV), conversion rates, attribution, and channel performance to support investment decisions. Collaborate with the Director of CRO, performance marketing teams, and Growth product managers to translate analytical findings into business recommendations. Influence prioritization and strategic decisions through clear, evidence-based insights while leaving final testing priorities and direction to Growth leadership. Establish scalable analytics standards, documentation, and a Growth experimentation playbook. Evaluate the experimentation technology stack and provide recommendations on whether to adopt external statistical tools or further develop existing internal capabilities. Requirements: Strong professional experience in data analytics, product analytics, growth analytics, or experimentation, ideally in a marketplace or digital business environment. Advanced SQL skills and the ability to independently build and maintain analytical models. Practical Python experience, particularly for statistical calculations, automation, and analytical tooling. Experience with dbt and Lightdash or comparable data transformation and business intelligence platforms. Strong statistical knowledge, including experimental design, power and sample-size calculations, significance testing, novelty effects, and interaction effects. Demonstrated experience designing, evaluating, and interpreting A/B tests and other experimentation methods. Understanding of experiment instrumentation, including event tracking, randomization, and tools such as Segment and Flagsmith. Experience building and analyzing acquisition-to-conversion funnels, with a strong understanding of CAC, LTV, conversion rates, and channel economics. Familiarity with attribution challenges, seasonality, and channel mix in growth and performance marketing environments. Ability to identify tracking and data-quality issues and ensure experiments produce reliable, actionable results. Daily use of AI tools such as Claude, ChatGPT, or similar solutions to accelerate SQL development, dbt workflows, statistical analysis, and experimentation processes. Ability to work independently, extend existing automation, and develop scalable self-service analytical solutions. Strong communication and stakeholder management skills, with the ability to influence product and marketing decisions without direct authority. Business-oriented mindset, intellectual curiosity, and the ability to translate complex statistical findings into clear recommendations. Comfortable challenging assumptions, communicating uncertainty, and prioritizing analytical rigor while maintaining a bias for action. Able to operate as a senior individual contributor without direct people-management responsibilities. Benefits: Annual base salary of $75,000–$100,000 USD. Fully remote working arrangement, with flexibility to manage your work environment and coordinate overlapping hours with US-based teams. Flexible paid time off, with an emphasis on results rather than hours worked. Access to established AI-powered experimentation tools and workflows, with the opportunity to expand their capabilities. Significant ownership and visibility as the first analyst dedicated entirely to Growth and Experimentation. Opportunity to shape experimentation standards, analytical processes, and growth decision-making across the business.