Staff Data Scientist, Marketplace Analytics

Your Impact on our Mission

Zocdoc's mission is simple and hard: give power to the patient. We are the nation's leading digital healthcare marketplace — connecting millions of patients to the right care, faster than ever.

The Data & Analytics team is at the center of how Zocdoc grows, evolves, and makes high-stakes decisions at scale. As a Sr/Staff Data Scientist you will be one of the most senior analytical voices at the company — a strategic thought partner to directors and senior leadership across Product, Marketing, Sales, Finance, Revenue, Operations, Customer Experience, and Platform.

You'll work on some of Zocdoc's most complex and ambiguous business problems — where the question itself isn't always defined, and the right method doesn't fit neatly into a playbook. You'll lead the analytical strategy, apply rigorous experimentation and causal inference frameworks at scale, and turn structured thinking into decisions that shape how Zocdoc grows.

This is a high-visibility, high-impact individual contributor role. Success here is measured not just by the quality of your analyses, but by the clarity, influence, and scalability of the thinking you bring to the teams around you.

Your day to day is

• Leading experimentation and analytical strategy across product, growth, monetization, marketing, and operational initiatives, often simultaneously
• Applying 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
• Partnering directly with directors and senior leaders to frame complex, cross-functional challenges and translate findings into clear recommendations that drive action
• Improving experimentation frameworks, metrics strategy, and analytical rigor across teams — developing scalable approaches for measurement, segmentation, forecasting, and incrementality
• Influencing roadmap prioritization and business strategy through rigorous analysis and precise communication of tradeoffs and uncertainty
• Mentoring and upskilling data scientists and analysts across the organization on experimentation, causal inference, and statistical best practices
• Partnering with Engineering, Product, and ML teams on instrumentation, data infrastructure, and analytics tooling
• Integrating generative AI tools into daily workflows to automate tasks, foster innovation, and maximize analytical productivity

You'll be successful in this role if you have

• An unrelenting desire to build more equitable, inclusive, and diverse workplaces — you view this role as an opportunity to create positive change
• 8+ years of experience in Data Science, Product Analytics, Applied Analytics, or Experimentation, with a track record of solving high-impact, cross-functional business problems. A Bachelor's degree in Computer Science, Data Science, Statistics, Applied Mathematics, Machine Learning, Artificial Intelligence, Economics, Physics, or a related field; a Master's degree or PhD in a quantitative field is a plus
• Experience partnering with senior stakeholders across Product, Marketing, Revenue, Sales, Operations, or Customer Experience organizations to drive strategy through data
• Strong experimentation experience at scale, including causal inference methodologies applied to real-world, often messy business settings
• Advanced SQL proficiency and strong Python fluency, with the ability to work across large, complex datasets
• The ability to integrate generative AI tools into daily workflows to automate tasks, foster innovation, and maximize productivity
• Familiarity with areas such as: pricing and monetization analytics, growth and retention strategy, customer segmentation and lifecycle, marketing attribution and incrementality, or operational and marketplace analytics
• Experience in fast-paced, high-growth environments — consumer technology, SaaS, fintech, marketplace, or platform companies is highly relevant
• Humility – you believe in treating all people with dignity and respect, regardless of title or tenure