Data Scientist
Ajah
CloudPandasPySparkPythonSQL
Data Analyst, Observational Causal Inference
The Walt Disney Company
full-time
Posted on:
Origin: • 🇺🇸 United States • California, New York
Visit company websiteSalary
💰 $95,300 - $127,800 per year
Job Level
Mid-LevelSenior
Tech Stack
CloudPythonSparkSQLTableau
About the role
- Apply advanced analytics techniques (data mining, data visualization, statistical analysis, causal inference, regression, machine learning, time-series forecasting) to large-scale, high-dimensional data to inform global business decisions
- Analyze customer behavior (e.g., genre preferences, viewing patterns) to identify unmet customer needs and untapped content opportunities
- Use advanced causal inference methodologies to quantify engagement and overall impact of sharing title licenses with internal and external platforms
- Predict content engagement to help guide acquisition decisions
- Optimize 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, including scoping projects, managing time & resources, and representing the product with business partners and executive leadership
- Maintain relationships with stakeholders and provide rapid, robust solutions and ad-hoc analysis support
- 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
- Nice-to-haves: Master of Science degree in related field; a passion for media and entertainment
