The Subscriber & Commerce Data Science team at Disney Streaming builds Machine learning models to optimize payment processes, detect and prevent fraud, and forecast customer lifetime value across our streaming platforms, including Disney+, Hulu and ESPN+. We play a key role in growing the business by increasing payment success, reducing fraud, improving retention, and enabling value measurement through user-level lifetime value (LTV) modeling.
Requirements
- Proficiency in SQL, Python (e.g. Pandas, NumPy, Scikit-learn, LightGBM); experience with distributed computing tools such as Spark or PySpark.
- Deep expertise in statistical modeling and machine learning, including Bayesian methods.
- Familiarity with tools like Databricks, Snowflake, Airflow, GitHub.
- Experience designing and analyzing A/B tests and other experiments.
- Experience with data visualization and exploration tools such as Tableau, Looker
- Ability to choose and justify appropriate modeling and statistical techniques for varied problems.
- 3+ years of experience developing and deploying machine learning models in production.
Responsibilities
- Develop, optimize, and maintain models for payment optimization, fraud detection, and LTV prediction.
- Build robust end-to-end ML workflows, including data collection, feature engineering, model development, and evaluation.
- Collaborate with Product and Engineering to deploy models into production environments and monitor performance.
- Design and analyze A/B tests and other experiments to assess model impact.
- Implement batch and real-time inference pipelines for fraud detection and payment optimization use cases.
- Analyze subscriber behavior, payment flows, and fraud patterns to generate actionable insights.
- Translate complex data into clear, data-driven recommendations to improve business outcomes.
Other
- Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
- Partner with stakeholders to translate business needs into machine learning problems.
- Collaborate with Engineering to improve data pipelines, experimentation frameworks, and model monitoring.
- Communicate insights effectively to technical and non-technical stakeholders.
- Comfortable working in fast-paced environments with evolving priorities.
- Excellent communication skills with both technical and non-technical audiences.
