Lead Data Scientist

Technology is at the heart of Disney's past, present, and future. Disney Entertainment and ESPN Product & Technology is a global organization of engineers, product developers, designers, technologists, data scientists, and more - all working to build and advance the technological backbone for Disney's media business globally.

The team marries technology with creativity to build world-class products, enhance storytelling, and drive velocity, innovation, and scalability for our businesses. We are Storytellers and Innovators. Creators and Builders. Entertainers and Engineers. We work with every part of The Walt Disney Company's media portfolio to advance the technological foundation and consumer media touch points serving millions of people around the world.

Here are a few reasons why we think you'd love working here:

+ Building the future of Disney's media:
Our Technologists are designing and building the products and platforms that will power our media, advertising, and distribution businesses for years to come.

+ Reach, Scale & Impact:
More than ever, Disney's technology and products serve as a signature doorway for fans' connections with the company's brands and stories. Disney+. Hulu. ESPN. ABC. ABC News...and many more. These products and brands
-and the unmatched stories, storytellers, and events they carry - matter to millions ofpeople globally.

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Innovation: We develop and implement groundbreaking products and techniques that shape industry norms, and solve complex and distinctive technical problems.

Product Engineering is a unified team responsible for the engineering of Disney Entertainment & ESPN digital and streaming products and platforms. This includes product engineering, media engineering, quality assurance, engineering behind personalization, commerce, lifecycle, and identity.

News & Entertainment Machine Learning (N&E ML) team is responsible for building robust data pipelines and advanced machine learning platforms that deliver personalized experiences to users across Disney's News & Entertainment portfolio including ABC News, ABC Entertainment, National Geographic, Marvel, and Disney Studios. Our services leverage machine learning models to enable real-time content personalization and targeted distribution across web, mobile, and connected TV platforms, ensuring that users receive the most relevant and engaging content tailored to their interests.

Our mission is to drive seamless, resilient, and low-latency personalized content delivery at scale, while continuously advancing our ML infrastructure and recommendation algorithms across one of the world's most iconic collections of entertainment brands.

As a Lead Data Scientist, you will act as the bridge between data and product decisions for the N&E ML Platform, ensuring that machine learning and personalization investments translate into measurable business impact across Disney's News & Entertainment portfolio. You will conduct exploratory data analysis, design and evaluate experiments, and define the success metrics used to judge model and product performance.

You will partner closely with Machine Learning Engineers on feature design, drift detection, and model evaluation, translating data insights into concrete, actionable recommendations for product and engineering stakeholders. Your impact will be measured by how effectively you turn data into decisions that improve the guest experience across ABC News, ABC Entertainment, National Geographic, Marvel, and Disney Studios.

** Responsibilities** :

+ Exploratory Data Analysis & Insights:
Conduct exploratory data analysis (EDA) across user, content, and engagement data to identify patterns, trends, and behavioral insights across the N&E portfolio (ABC News, ABC Entertainment, National Geographic, Marvel, and Disney Studios).

+ Experimentation & A/B Testing:
Design, implement, and evaluate A/B tests and other controlled experiments to validate product and personalization changes, applying sound statistical methodology to ensure results are reliable and actionable.

+ Success Metrics & Model Evaluation:
Define success metrics for ML-driven features and measure model performance against…