Data Scientist II

The Walt Disney Company•Santa Monica, CA

16d•$114,900 - $161,300

About The Position

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 t hrough user-level lifetime value (LTV) modeling. We're hiring a Data Scien tist 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.

Requirements

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 .

Nice To Haves

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.

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 to 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 improv e business outcomes.
Partner with stakeholders to translate business needs into machine learning problems.
Collaborate with E ngineering to improve data pipelines , experimentation frameworks, and model monitoring.
Communicate insights effectively to technical and non-technical stakeholders .