Job Description
- Description:
- 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.
- Collaborate closely with cross-functional partners across the Business, architect and execute sophisticated experiments that optimize every aspect of the subscriber journey—from initial acquisition through long-term retention and revenue growth.
- Tackle complex business challenges that directly impact millions of subscribers across Disney+, Hulu, and ESPN.
- 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.
- 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.
- Requirements:
- 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: Masters or PhD in quantitative field with an emphasis on experimentation or causal inference.
- Preferred: Experience applying strategic thinking to analyze market trends and consumer insights, with preference for candidates who have worked with subscription-based business models.
- Preferred: Familiarity with data platforms and applications such as Databricks, Jupyter , Snowflake, and Github .
Benefits:
A bonus and/or long-term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits, dependent on the level and position offered.
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