Hybrid Data Analyst – Observational Causal Inference

at The Walt Disney Company

• Job title: Data Analyst – Observational Causal Inference
• Job type: Full Time
• Experience level: Mid level, Senior
• Salary: $99,900 - $133,900 per year
• Degree requirement: Bachelor's Degree
• Tech skills: Cloud, Python, Spark, SQL, Tableau
• Location requirements: Hybrid, New York City, United States

About the role

• Data Analyst optimizing strategic decisions based on data science principles for Direct to Consumer Data & Analytics organization. Collaborating on projects involving content management, acquisition, and subscriber health.

Responsibilities

• Apply advanced analytics techniques to large-scale, high-dimensional data in order to inform global business decisions
• Analyze customer behavior to identify unmet customer needs and untapped content opportunities
• Use advanced causal inference methodologies to quantify engagement and overall impact of sharing title licenses
• Predict content engagement to help guide acquisition decisions
• Optimize our content launch and episode release strategy
• Test merchandising strategies to optimize engagement and retention
• Ideate and develop new metrics and KPIs, measuring content performance, engagement and churn for strategic decision-making
• Support complex projects, workstreams, and new initiatives & capabilities.
• Maintain relationships with stakeholders while understanding their needs
• Provide ad-hoc analysis support for stakeholders to help move the business forward
• Effectively communicate actionable results through compelling data storytelling across the organization

Requirements

• Bachelor’s degree in Data Science, Mathematics, Statistics, Computer Science, Applied Economics, or a related field.
• 3+ years of experience in analytics, machine learning model development, and data analysis using Python and/or R.
• Proficient in querying cloud-hosted databases with SQL and engineering data solutions using technologies like Databricks, S3, and Spark.
• Applied expertise in observational causal inference methods (e.g., difference-in-difference, propensity score matching) for non-experimental settings.
• Skilled in statistical and machine learning techniques including time-series forecasting, regression, decision trees, and clustering.
• Strong data storytelling abilities across verbal, written, and visual formats.
• Effective communicator with both technical and non-technical audiences, capable of explaining model behavior and algorithmic decisions.
• Familiar with data exploration and visualization tools such as Looker, Tableau, and JupyterLab/Notebook.
• Demonstrated independence and creativity in solving open-ended problems.

Benefits

• A bonus and/or long-term incentive units may be provided as part of the compensation package
• Full range of medical, financial, and/or other benefits dependent on the level and position offered