Job Description
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.
We're hiring a Data Scientist to help design, build and deploy machine learning solutions that solve key business challenges. In this role, you’ll work closely with Product, Engineering, Analytics and Finance to deliver models that enhance the customer experience and drive measurable business impact.
Responsibilities Machine Learning & Modeling - 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.
Experimentation & Deployment - 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.
Insights & Strategy - Analyze subscriber behavior, payment flows, and fraud patterns to generate actionable insights.
- Translate complex data into clear, data-driven recommendations to improve business outcomes.
Cross-functional Collaboration - 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 - M.S. 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 hiring range for this position in New York, NY is $145,500 to $195,000 per year. Santa Monica, CA and Glendale, CA is $138,900 to $186,200 per year, San Francisco, CA is $152,100 to $203,900 per year. The base pay actually offered will take into account internal equity and also may vary depending on the candidate’s geographic region, job-related knowledge, skills, and experience among other factors. A bonus and/or long-term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits, dependent on the level and position offered.
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