Lead Data Scientist - Experimentation

Join Disney's Direct to Consumer Experimentation and Causal Inference Data Science team as a Lead Data Scientist, where you'll transform complex data into strategic business decisions that shape the future of streaming entertainment. Collaborating closely with cross-functional partners across the Business, you'll architect and execute sophisticated experiments that optimize every aspect of the subscriber journey—from initial acquisition through long-term retention and revenue growth. 

As part of Disney's rapidly evolving streaming ecosystem, you'll tackle complex business challenges that directly impact millions of subscribers across Disney+, Hulu, and ESPN. Your insights will shape Product roadmaps, pricing strategies, and user experience optimizations that drive measurable business growth. 

What You'll Do 

• Design and Execute Experiments: Lead end-to-end A/B testing initiatives and Geo Experiments, from hypothesis formation and experimental design to statistical analysis and business recommendations 

• Apply Causal Inference Methods: Leverage advanced techniques including difference-in-differences, instrumental variables, propensity score analysis, and other quasi-experimental designs to extract actionable insights from observational data 

• Build Scalable Solutions: Develop experimentation and causal inference tools and frameworks that can scale across Disney's businesses 

• Deliver Strategic Insights: Partner with stakeholders to identify optimization opportunities and translate complex analytical findings into clear business recommendations 

• Drive Innovation: Be a thought leader on robust and rigorous analysis throughout the Data Intelligence and Analytics team 

• Influence Executive Decisions: Present findings and recommendations to senior leadership, effectively communicating statistical concepts to non-technical stakeholders 

Minimum Qualifications 

• Bachelor’s degree in advanced Mathematics, Statistics, Data Science or comparable field of study 

• 7+ years of experience conducting strategic analyses and communicating insights to drive decision-making. 

• Expertise in Python, R, or similar languages, including experience building software packages for statistical analysis. 

• Expertise in SQL. 

• Proficient in analyzing data and developing ML models using Python (with ML frameworks like LGBM, scikit-learn, etc.). 

• Strong background in statistical modeling: regression, classification, time series forecasting, causal inference, and other techniques. 

• Highly collaborative with excellent written and verbal communication skills and demonstrated experience presenting directly to Executive stakeholders 

• Demonstrated ability to translate complex data into clear and actionable narratives, and the ability to communicate opportunities and challenges to multiple stakeholders. 

• Robust knowledge of causal inference approaches such as propensity scores, synthetic controls, difference-in-differences, doubly robust methods, meta learners, and uplift modeling. 

• Deep understanding of assumptions required for causal inferences, including the foundational statistical concepts that underpin the approaches. 

• Proven ability to manage end-to-end experimentation and causal inference analyses, from initial requirements to impactful outcomes. 

• Exceptional curiosity and a drive for insights that impact business outcomes. 

Preferred Qualifications 

• Masters or PhD in quantitative field with an emphasis on experimentation or causal inference. 

• Experience applying strategic thinking to analyze market trends and consumer insights, with preference for candidates who have worked with subscription-based business models. 

• Ability to adapt quickly in a fast-moving environment with shifting priorities. 

• Familiarity with data platforms and applications such as Databricks, Jupyter, Snowflake, and Github.