Data Scientist II

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 monitorperformance.

Design and analyze A / B tests and other experiments to assess model impact.

Implement batch and real-timeinference 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 improvebusiness 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.

Communicateinsights 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.

#DINSEYANALYTICS

#DISNEYTECH

The hiring range for this position in Santa Monica, CA is $114,900 to $154,100 per year and in New York City, NY is $120,300 to $161,300. 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.

Job ID : 10128634

Location : Santa Monica,California

Job Posting Company : Disney Direct to Consumer

The Walt Disney Company and its Affiliated Companies are Equal Employment Opportunity employers and welcome all job seekers including individuals with disabilities and veterans with disabilities. If you have a disability and believe you need a reasonable accommodation in order to search for a job opening or apply for a position, email Candidate.Accommodations@Disney.com with your request. This email address is not for general employment inquiries or correspondence. We will only respond to those requests that are related to the accessibility of the online application system due to a disability.