Lead Data Analyst, Rover

• 6+ years of analytics experience, with a track record of turning complex analysis into business decisions and measurable outcomes
• Advanced SQL and strong statistical analysis skills in Python or R, including reviewing others' code and collaborating with engineering on data models
• Proven experimental design and A/B testing experience, including applying causal inference or geo-based methods and explaining their limitations
• Experience independently leading large analytical initiatives, managing stakeholder expectations and timelines, and influencing strategy across functions
• Clear communicator with technical and non-technical audiences, who is able to shape compelling data narratives through analysis and data visualization
• Interested? We'd love to hear from you, even if you don't bring every nice-to-have

Desirable

• Join Marketing Analytics as a senior individual contributor focused on Search Engine Marketing (SEM) to lead paid search analytics for Rover globally. You'll shape how we measure and optimize acquisition across our growing portfolio of brands and markets
• As a key thought partner for SEM leaders, Finance, Data Science, and Analytics Engineering, you'll set the analytical agenda and stay hands-on with the analysis. A central question: when our brands compete for the same searches, how do we know if we have grown the business as a whole?
• Guide bidding and investment across brands. Evaluate spend allocation, marginal returns, customer lifetime value (LTV), and payback. Help SEM teams assess cross-brand cannibalization and identify where additional spend generates incremental growth
• Lead experimentation and incrementality. Design and analyze channel tests, geo-split experiments, and multibrand tests. Choose appropriate methods, assess statistical power and uncertainty, and turn results into recommendations on bidding and spend
• Explain performance and identify opportunities. Diagnose changes in acquisition cost, conversion, and return on ad spend (ROAS). Translate auction dynamics, customer behavior, and marketplace conditions into clear recommendations that lead to action
• Strengthen attribution and reporting. Reconcile platform and internal measurement, distinguish attributed results from incremental growth, and build trusted dashboards. Partner with engineering and Data Science on data quality, conversion signals, and customer-value inputs
• Own the SEM analytics roadmap. Identify important questions, challenge assumptions, and prioritize by business impact. Build learning agendas for different markets and lead stakeholders toward decisions grounded in evidence and clear trade-offs
• Raise the team's analytical standards. Mentor analysts, review SQL and analytical methods, and develop reusable tools and documentation. Balance statistical rigor with business needs