Job Details

Overview

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.

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 in 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.
• 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.

Basic Qualifications

• Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
• 3+ years of experience developing and deploying machine learning models in production.
• Proficiency in SQL, Python (e.g., Pandas, NumPy, Scikit-learn, LightGBM); experience with distributed computing tools such as Spark or PySpark.

Preferred Qualifications

• MS or Ph.D. in a quantitative discipline.
• 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.
• Comfortable working in fast-paced environments with evolving priorities.
• Excellent communication skills with both technical and non-technical audiences.