Overview In this Senior Data Scientist role, you will drive provider growth within Zocdoc's leading two-sided marketplace by delivering marketplace insights, experimentation, and predictive models. You'll partner with Sales, Finance, and Product to shape decisions that affect user experience and business outcomes. Expect end-to-end work-from problem framing to model building and presenting actionable recommendations to leadership. You'll apply applied statistics to real-world healthcare access challenges, focusing on supply growth, acquisition, retention, and lifetime value.
Compensation / Benefits- Flexible, hybrid work environment
- Unlimited vacation
- 100% paid employee health benefits
- Commuter benefits
- 401(k) with employer match
- Sabbatical leave for 5+ years of service
Responsibilities- Translate insights into actionable business recommendations with commercial and product partners
- Build models and analyses to understand supply and demand, improve revenue (LTV, CAC), and predict churn for commercial strategy
- Collaborate with Finance and business partners to improve forecasting and data-driven decision-making
- Design and measure experiments (A/B tests, Diff-in-Diff, synthetic control, propensity score matching) to assess impact
- Partner with Sales, Revenue Operations, Finance, and Systems to ensure data quality and consistency in tools like Salesforce
Key requirements- 7+ years in data science or related field
- Strong foundation in statistics, math, economics, CS, or engineering
- Proven success in developing LTV, survival, uplift, or similar customer prediction models
- Expertise in SQL and Python (pandas, NumPy, scikit-learn, statsmodels)
- Experience with experimentation and causal inference methods
- Ability to turn ambiguous problems into clear analyses and influence stakeholders
- Cross-functional product collaboration and mentoring junior data scientists
- Strong problem-solving and analytical thinking
- Ability to translate data insights into business decisions
- Effective cross-functional collaboration and mentoring
- SQL
- Python (pandas, NumPy, scikit-learn, statsmodels)
- Experiment design and causal inference (A/B testing, Diff-in-Diff, synthetic control, propensity score matching)
