Walt Disney
Location
Santa Monica, CA | United States
Job description
On any given day at Disney Entertainment & ESPN Technology, we’re reimagining ways to create magical viewing experiences for the world’s most beloved stories while also transforming our media business for the future. Whether that’s evolving our streaming and digital products in new and immersive ways, powering worldwide advertising and distribution to enhance flexibility and efficiency, or delivering Disney’s unmatched entertainment and sports content, every day is a moment to make a difference to partners and to hundreds of millions of people around the world.
A few reasons why we think you’d love working here:
Building the future of Disney’s media: DE&E Technologists are designing and building the infrastructure that will power our media, advertising, and distribution businesses for years to come.
Reach & Scale: The products and platforms this group builds and operates delight millions of consumers every minute of every day – from Disney+ and Hulu, to ABC News and Entertainment, to ESPN and ESPN+, and much more.
Innovation: We develop and implement groundbreaking products and techniques that shape industry norms and enhance how audiences experience sports, entertainment & news.
Our team develops and maintains recommendation and personalization algorithms for Disney Streaming’s suite of streaming video apps, notably Disney+ and Hulu. 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 in content recommendations. You will be expected to lead recommendation and personalization algorithm research, development, and optimization for product areas, and to coordinate requirements and manage stakeholder expectations with Product, Engineering, and Editorial teams. You will be expected to help meet KPIs for product areas and to set and meet deadlines for external and internally facing tools, such as offline evaluation tools for pre-production algorithms. 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 content recommendation.
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 Qualifications:
7+ years of analytical and/or data engineering experience
7+ years of experience developing machine learning models and performing data analysis with Python or R
5+ years writing production-level, scalable code (e.g. Python, Scala)
5+ 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 (AWS, Docker, Airflow) 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
Additional 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 such as Snowflake
Building streaming data pipelines using Kafka, Spark, or Flink
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