Senior Data Scientist

About the Role The Data Scientist, Product Growth and Experimentation will help drive decision making in support of our Product Led Growth function. This role sits at the intersection of Data, Product, Growth, and GTM. You will work deeply embedded within the Product and Engineering teams focused on PLG. Your focus will be illuminating the user journey from initial interest to fully adopted and all the steps in between. You will surface friction points in the onboarding process and identify behaviors that are indicative of successful adoption. You will evaluate interventions that help us to bring the value of Temporal to more users. You will work effectively across both large data, clean data sets and data sets with constraints in duration and sample size to provide well-informed recommendations and conclusions. You will bring deep knowledge and practical experience with statistical methods including matched comparisons, quasi-experimental methods, and uncertainty measurements to help overcome these constraints. The ideal candidate possesses strong analytical judgment and practical product sense. Ambiguous business questions do not intimidate you. You have a keen sense for when additional data or analysis could materially change a conclusion, and when it wouldn’t. You apply analytical rigor while balancing what is functionally required to advance a project. Your aim is to drive better decisions and faster learning through measurable customer outcomes. What You’ll Do Act as an embedded Data partner to Product, Design, and Engineering, helping shape strategy, product proposals, and experiments from the earliest stages. Build analytical frameworks across the full PLG journey from acquisition and activation through engagement, adoption, expansion, monetization, and durability Use funnel, cohort, journey, and sequence analysis to identify friction and opportunities for improvement. Identify the behaviors and milestones that predict conversion, production readiness, expansion, churn, and long-term customer success, and translate them into leading indicators for successful or stalled accounts. Analyze feature discovery and adoption patterns to recommend interventions that increase successful adoption and expansion. Design and evaluate experiments and product changes, using matched comparisons, pre/post-intervention analysis, causal methods, and other appropriate techniques when conventional A/B testing is not practical. Clearly distinguish observed results from assumptions, quantify uncertainty, and explain the confidence behind each conclusion. Help prioritize growth opportunities based on potential impact, confidence, effort, and measurement feasibility. Build reusable datasets, metrics, dashboards, and analytical tools that reduce ad hoc work and make insights accessible to Product, Marketing, Sales, and leadership. Partner with Engineering and Data Engineering to improve event instrumentation, data quality, and the analytical models required for rel