Senior Data Scientist, Marketplace

Healthcare should work for patients, but it doesn’t. In their time of need, they call down outdated insurance directories. Then wait on hold. Then wait weeks for the privilege of a visit. Then wait in a room solely designed for waiting. Then wait for a surprise bill. In any other consumer industry, the companies delivering such a poor customer experience would not survive. But in healthcare, patients lack market power. Which means they are expected to accept the unacceptable.

Zocdoc’s mission is to give power to the patient. To do that, we’ve built the leading healthcare marketplace that makes it easy to find and book in-person or virtual care in all 50 states, across +200 specialties and +12k insurance plans. By giving patients the ability to see and choose, we give them power. In doing so, we can make healthcare work like every other consumer sector, where businesses compete for customers, not the other way around. In time, this will drive quality up and prices down. 

We’re 18 years old and the leader in our space, but we are still just getting started. If you like solving important, complex problems alongside deeply thoughtful, driven, and collaborative teammates, read on.

The Senior Data Scientist role on the Marketplace Insurance team exists to independently deliver rigorous, high-impact analyses that inform product and business decisions within Zocdoc’s core marketplace. As part of the Marketplace Analytics team, this role supports efforts to drive patient demand, optimize the search and booking experience, and maintain a healthy, high-performing two-sided marketplace.

In this role, you will focus on the Insurance product area, helping patients better understand their coverage—one of the most critical factors in successfully matching patients with providers. You will partner closely with Product, Engineering, Design, and cross-functional stakeholders to frame complex analytical problems, define how we measure patient understanding and success, and identify opportunities to reduce friction in insurance-related workflows.

You will own end-to-end execution of analyses and experiments, applying strong statistical and analytical methods to generate clear, defensible recommendations that guide product prioritization and strategy. Success in this role requires sound analytical judgment and the ability to balance rigor, speed, and ambiguity, along with a strong foundation in technical execution and analytical craftsmanship.

• Track record of independently executing end-to-end analyses to answer complex product and business questions, from problem framing through insight delivery
• Select and apply appropriate analytical and statistical methods based on the decision context
• Design, execute, and analyze experiments end-to-end, evaluating experiment quality and clearly communicating results, limitations, and uncertainty
• Lead structured investigations into metric changes by identifying plausible drivers, prioritizing hypotheses, and defining focused analytical plans
• Define success, guardrail, and diagnostic metrics for features or initiatives within their domain, with alignment from senior stakeholders
• Partner with Product, Engineering, and Data Engineering to ensure measurement readiness, analytical QA, and correct interpretation of results
• Anticipate and surface data quality, instrumentation, or measurement issues that could materially affect conclusions, and work with partners to resolve them
• Communicate analytical findings and recommendations clearly to stakeholders, focusing on implications, tradeoffs, and next steps
• Document analytical context, methods, and decisions in a way that enables reuse and learning across the team
• Provide informal mentorship and guidance to Data Scientists, supporting their analytical development and quality of work

• Quality, rigor, and robustness of analytical outputs, as measured through review feedback and stakeholder trust in conclusions
• Effectiveness of analyses in informing product and business decisions, including clarity of recommendations and handling of uncertainty
• Reliability and timeliness of delivery across multiple concurrent analyses or projects
• Contribution to raising analytical quality within the team through feedback, review, and knowledge sharing

• Bachelor’s degree in a quantitative field such as Statistics, Mathematics, Economics, Computer Science, Engineering, or a related discipline
• 3–6 years of experience in data science roles, or equivalent experience through applied industry or research work.
• Strong understanding of statistical concepts, including experimentation, causal reasoning, and predictive modeling
• Expertise in SQL and advanced knowledge of an analytical programming language such as Python or R
• Experience owning end-to-end analytical workflows, including data validation, method selection, and result interpretation
• Ability to operate independently on moderately ambiguous problems and translate business questions into analytical approaches
• Strong written and verbal communication skills, with the ability to clearly explain analytical results, limitations, and implications
• Comfort providing feedback to peers and supporting the development of more junior data scientists
• Master’s degree or PhD in a quantitative field is a plus, but not required.