The Walt Disney Company (France)
Data Scientist II
The Walt Disney Company (France), Santa Monica, California, United States, 90403
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.
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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.
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