Data Scientist

Key Responsibilities

• 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.
• Advanced Statistical & Causal Inference: Apply deep knowledge of experimental design, regression, classification, causal inference and ensure proper assumptions.
• 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.

Qualifications

• Bachelors in Statistics, Economics, Computer Science, Engineering, Mathematics, Physics, or a related field + 7 years of experience with an emphasis on experimentation or causal inference.
• Strong background in statistical modelling: regression, classification, time series forecasting, causal inference, and other techniques.
• Robust knowledge of causal inference approaches such as propensity scores, synthetic controls, difference-in-differences, doubly robust methods, meta learners, and uplift modeling.
• Expertise in A/B test design, execution, statistical modeling, and sophisticated causal inference techniques.
• Experience managing multiple testing scenarios and controlling false discovery rates.
• Ability to deploy both Bayesian and frequentist statistical approaches.
• 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.
• Advanced skills in Python and/or R—including development of statistical analysis packages, and use of ML frameworks (e.g., scikit-learn, LGBM).
• Strong communication skills for translating complex data into actionable narratives and presenting confidently to technical and non-technical audiences, including senior executives.