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Zocdoc is the nation's leading digital healthcare marketplace, connecting millions of patients to care across all 50 states and 200+ specialties. The Data & Analytics team sits at the center of how the company grows and makes high-stakes decisions at scale.
As a Staff Data Scientist, you will be one of the most senior analytical voices at Zocdoc—a strategic thought partner to directors and senior leadership across Product, Marketing, Sales, Finance, Revenue, Operations, Customer Experience, and Platform. You'll work on the company's most complex and ambiguous business problems where the question itself often isn't clearly defined.
Your day-to-day includes leading experimentation and analytical strategy across product, growth, monetization, and marketing initiatives. You'll apply advanced causal inference methodologies (difference-in-differences, synthetic controls, propensity methods, Bayesian experimentation) to real business questions where traditional A/B testing may not be feasible. You'll partner directly with directors and senior leaders to frame cross-functional challenges and translate findings into clear, actionable recommendations.
You'll also improve experimentation frameworks, metrics strategy, and analytical rigor across teams—developing scalable approaches for measurement, segmentation, forecasting, and incrementality. A key part of the role is influencing roadmap prioritization and business strategy through rigorous analysis and precise communication of tradeoffs and uncertainty. You'll mentor and upskill data scientists and analysts across the organization on experimentation, causal inference, and statistical best practices, and partner with Engineering, Product, and ML teams on instrumentation and analytics tooling.
This is a high-visibility, high-impact individual contributor role where success is measured by the quality of your analyses, the clarity and influence of your thinking, and the scalability of your approaches. You should be energized by working at the intersection of product strategy, growth, monetization, and experimentation, comfortable operating with ambiguity while maintaining rigor, and passionate about connecting technical work to real business outcomes.
