Disney Entertainment & ESPN Technology
On any given day at Disney Entertainment & ESPN Technology, we are reimagining ways to create magical viewing experiences for the world’s most beloved stories while also transforming Disney’s media business for the future. Whether that is evolving our streaming and digital products in new and immersive ways, powering worldwide advertising and distribution to maximize 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 would love working for Disney Entertainment & ESPN Technology
- Building the future of Disney’s media business: DE&E (Disney Entertainment & ESPN) Technologists are designing and building the infrastructure that will power Disney’s 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 execute groundbreaking products and techniques that shape industry norms and enhance how audiences experience sports, entertainment & news.
The vision of the Machine Learning (ML) Engineering team at Disney is to drive and enable ML usage across several domains in heterogeneous language environments and at all stages of a project’s life cycle, including ad-hoc exploration, preparing training data, model development, and robust production deployment. The team is invested in continual innovation on the ML infrastructure itself to carefully orchestrate a continuous cycle of learning, inference, and observation while also maintaining high system availability and reliability. We seek to maximize the positive business impact of all ML at Disney streaming by supporting key product functions like personalization and recommendation, fraud and abuse prevention, capacity planning, subscriber growth and lifecycle intelligence, and so on.
In this role, you will be expected to lead recommendation and personalization algorithm research, development, implementation, and optimization for product areas, and work on event and context processors to federate data, infrastructure and tooling to enable event-driven ML pipelines. You will own and expand part of our central feature store that powers ML use cases in domains like recommendations, search and fraud. You will work on cross-functional projects and push the envelope on data and ML infrastructure.
What You Will Do
- Algorithm Development and Maintenance: Utilize cutting edge machine learning methods to deploy and 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.
What You Will Bring
Basic Qualifications:
- 7+ years of relevant experience developing machine learning models, performing large-scale data analysis, and/or data engineering experience.
- 7+ years of experience 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.
- 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.
- Ability to gauge the complexity of machine learning problems and a willingness to execute simple approaches for quick, effective solutions as appropriate.
- Familiar with metadata management, data lineage, and principles of data governance.
- Experience loading and querying cloud-hosted databases.
- Building streaming data pipelines using Kafka, Spark, or Flink.
- Experience with: AWS, Docker, Airflow, Databricks.
Required Education:
- Bachelor’s Degree in Computer Science, Mathematics, Statistics, or related quantitative field or comparable field of study, and/or equivalent work experience.
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