Senior Data Scientist – Experimentation & Causal Inference

Umanist Staffing • Glendale, CA

2d

About The Position

This role focuses on designing and executing experiments, applying advanced statistical and causal inference techniques, building scalable solutions, delivering strategic insights, and influencing executive decisions. The Senior Data Scientist will lead end-to-end A/B testing and Geo Experiments, from hypothesis formation to business recommendations. They will leverage deep knowledge of experimental design, regression, classification, and causal inference methods. A key aspect of the role is developing and scaling experimentation and causal inference tools across Disney's businesses. The position requires partnering with stakeholders to identify optimization opportunities and translating complex analytical findings into clear, actionable business recommendations. A significant part of the role involves presenting findings and recommendations to senior leadership, effectively communicating statistical concepts to non-technical audiences.

Requirements

• 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 modeling: 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.
• Proficient in conducting sample size calculations, power analysis, and minimum detectable effect estimation.
• 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.

Nice To Haves

• MS in computer science, statistics, math or a related quantitative field +5 years of relevant experience OR PhD + 3 years of relevant experience with an emphasis on experimentation or causal inference.
• Experience with ETL and data engineering: data extraction, transformation, integration, and quality controls for analytics at scale.
• Skilled in production deployment and monitoring of data science solutions, including CI/CD pipelines, automated reporting, and ongoing experiment/model monitoring.
• Familiarity with data platforms and applications such as Databricks, Jupyter, Snowflake, and Github.
• Strong strategic business insight, preferably in subscription-based business models, with ability to apply experimentation and analytics to market trends and consumer insights.
• Proven track record of leadership and stakeholder/project management, including influencing cross-functional teams and delivering high-impact outcomes.
• Adept at adapting quickly to shifting priorities in a fast-moving environment while maintaining quality.
• Drive and maintain a culture of quality, innovation and experimentation.
• Demonstrated experience mentoring colleagues on best practices and technical concepts for building large scale solutions.

Responsibilities

• Lead end-to-end A/B testing initiatives and Geo Experiments, from hypothesis formation and experimental design to statistical analysis and business recommendations.
• Apply deep knowledge of experimental design, regression, classification, causal inference (difference-in-differences, propensity scores, instrumental variables), and ensure proper assumptions.
• Develop experimentation and causal inference tools and frameworks that can scale across Disney's businesses.
• Partner with stakeholders to identify optimization opportunities and translate complex analytical findings into clear business recommendations.
• Present findings and recommendations to senior leadership, effectively communicating statistical concepts to non-technical stakeholders.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior