Job Description

Job Summary:

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 .

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The hiring range for this position in Santa Monica, CA and Glendale, CA is $152,200 - $204,100 per year, in NYC area is $159,500-$213,900, and in San Francisco area $166,800-$223,600. The base pay actually offered will take into account internal equity and also may vary depending on the candidate’s geographic region, job-related knowledge, skills, and experience among other factors. 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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