Disney Entertainment & ESPN Technology
We are reimagining ways to create magical viewing experiences for beloved stories and transforming Disney's media business for the future.
Our team is designing and building infrastructure to power Disney's media, advertising, and distribution businesses for years to come.
We are seeking a Senior Machine Learning Engineer to lead recommendation and personalization algorithm research, development, implementation, and optimization for product areas.
In this role, you will collaborate with product and business stakeholders to identify and define new personalization opportunities and work on cross-functional projects to push the envelope on data and machine learning 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 (eg Python, Scala)
- 5+ years of experience developing algorithms for deployment to production systems
- In-depth understanding of modern machine learning (eg 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
The base pay 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.
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