Senior Data Scientist - Experimentation and Causal Inference

The Walt Disney Company-posted 3 days ago

Full-time • Senior

San Francisco, NY

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Join Disney's Direct to Consumer Experimentation and Causal Inference Data Science team as a Sr 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.

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

Bachelors in Statistics, Economics, Computer Science, Engineering, Mathematics, Physics, or a related field.
5+ 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.
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.

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.

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