thumbtack - Senior Data Scientist, Lifecycle

• Build the Lifecycle measurement framework — define lifecycle health metrics for both homeowner and pro, design experiments with appropriate rigor, and navigate attribution and contamination challenges in ambiguous measurement contexts. • Build relationships with cross-functional teams across pro and homeowner lifecycle. Understand strategic challenges and contextualize them to use data to unlock new growth levers and inform product roadmap. • Synthesize evidence across data sources to produce insights that shift senior leaders’ mental models — going beyond what happened to explain why and what to do next across the full pro and customer lifecycle; provide analytical rigor for the evolution toward intelligent, customer-centric decisioning and personalization. • Serve as the tech lead for the Lifecycle — influence senior cross-functional partners, drive pillar-level alignment through insights and partnership. Be opinionated about strategic decisions and trade-offs based on your deep domain knowledge. • Continue to evolve workflow automation and self-serve. Discovering new ideas, implementing POCs and coaching fellow DS team members. • IN ORDER TO BE SUCCESSFUL, YOU MUST BRING • Bachelor’s or Master’s degree in Statistics, Math, Economics, Computer Science, or another STEM field. • 5+ years of experience in a highly analytical role, with strong proficiency in SQL and Python for working with large, complex datasets. • Cultivated a reputation as a trusted advisor to senior leadership and diverse cross-functional teams esp. managing non-technical stakeholders, leveraging insights to build consensus and influence strategy across product, marketing, and engineering, successfully influencing without direct authority. • Proven track record of independently leading long-term, ambiguous analytical initiatives with measurable business impact — not just executing defined analyses but scoping and shaping the work. • Deep expertise in experimental design, causal inference, and statistical methods — with the ability to navigate ambiguity in measurement contexts and communicate uncertainty clearly to non-technical stakeholders. • Experience building data products — dashboards, self-service tools, or reusable analytical frameworks — that scale beyond your immediate team. • Strong business acumen and ability to connect data findings to lifecycle strategy, making crisp prioritization recommendations under uncertainty.