Role Location:

This is an on-site role requiring 4 days in-person at designated office location.

Disney Entertainment and ESPN Product & Technology

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 of people globally.

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.

Job Summary:

Our team designs and builds models that directly shape the user experience – powering personalization and engagement across our Disney Streaming’s suite of streaming video apps, notably Disney+ and Hulu. With a strong product mindset and a focus on usability, we ensure every ML-driven product enhances how users discover, interact, and enjoy our experiences.

As a member of this team you will collaborate across Engineering, Product, and Data teams to apply machine learning methods to meet strategic product personalization goals, explore innovative, cutting edge techniques that can be applied to recommendations, and constantly seek ways to optimize operational processes.

This is an Individual Contributor role. You will be expected to lead recommendation and personalization algorithm research, development, and productionization for product areas, and to coordinate requirements and manage stakeholder expectations with Product, Engineering, and Editorial teams. As an IC, you will also be responsible for helping to set the roadmap for algorithmic work — not only for how to approach product requests for new recommendation features, but for helping to drive larger company objectives in the areas of personalization and recommendations.

Responsibilities and Duties of the Role:

• Algorithm Development and Maintenance: Utilize cutting edge machine learning methods to develop algorithms for personalization, recommendation, and other predictive systems; maintain algorithms deployed to production and be the point person in explaining methodologies to technical and non-technical teams

• Feature Engineering and Optimization: Develop and maintain ETL pipelines using orchestration tools such as Airflow and Jenkins; deploy scalable streaming and batch data pipelines to support petabyte scale datasets

• Development Best Practices: Maintain existing and establish new algorithm development, testing, and deployment standards

• Collaborate with product and business stakeholders: Identify and define new personalization opportunities and work with other data teams to improve how we do data collection, experimentation and analysis

Required Education, Experience/Skills/Training:

Basic Qualifications

• 5+ years of experience developing machine learning models, performing large-scale data analysis, and/or data engineering experience

• 5+ years writing production-level, scalable code (Python, SQL)

• 3+ years of experience developing algorithms for deployment to production systems

• In-depth understanding of modern machine learning (e.g. deep learning methods), models, and their mathematical underpinnings

• Experience deploying and maintaining pipelines and in engineering big-data solutions using technologies like Databricks, S3, and Spark

• Ability to gauge the complexity of machine learning problems and a willingness to execute simple approaches for quick, effective solutions as appropriate

• Strong written and verbal communication skills

Preferred Qualifications

• MS or PhD in statistics, math, computer science, or related quantitative field

• Production experience with developing content recommendation algorithms at scale

• Experience building and deploying full stack ML pipelines: data extraction, data mining, model training, feature development, testing, and deployment

• Familiar with metadata management, data lineage, and principles of data governance

• Experience loading and querying cloud-hosted databases

Experience with:

• AWS, Databricks

Required Education

• Bachelor’s Degree in Computer Science, Math, Statistics, or related quantitative field
  
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The hiring range for this position in New York, NY is $148,700 - $199,400 per year and in Santa Monica, CA is $141,900 - $190,300. 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.