Disney Entertainment’s (DE) Cross-Media Measurement and Advanced Analytics organization is looking to solve data strategy, cross-platform content measurement, marketing effectiveness, and inventory forecasting problems
Requirements
- Advanced coding skills in Python and SQL; familiarity with a strongly typed language and software engineering best practices (version control, CI/CD, unit testing).
- Hands-on experience with cloud-native data platforms and distributed frameworks (BigQuery, Snowflake, Databricks) and orchestration tools (Airflow, Dagster).
- Proven ability to build end-to-end ML workflows: feature engineering, model training, containerization, monitoring, and performance tuning.
- Working knowledge of data privacy standards (GDPR, CCPA) and experience applying them to identity or audience datasets.
- Experience with deep learning, generative AI, or retrieval-augmented systems (PyTorch, vector databases) and real-time pipelines (Kafka, Pub/Sub, Kinesis).
- Familiarity with modern MLOps stacks (MLflow, Kubeflow, Vertex AI, SageMaker) and model governance practices.
- Relevant certifications (Google ML Engineer, AWS ML Specialty, or equivalent).
Responsibilities
- Data Pipeline Development: Build and optimize scalable pipelines using orchestration tools (Airflow/Dagster) to ingest, transform, and deliver cross-media datasets.
- Feature Engineering & Data Prep: Create high-quality features from petabyte-scale data, manage metadata, and optimize storage/performance in Snowflake or Databricks.
- Model Development: Design, train, and deploy ML models for audience identity, look-alike modeling, and cross-platform measurement; write clean, testable Python/SQL code and containerize workloads via Docker/Kubernetes.
- MLOps & Monitoring: Implement CI/CD, model versioning, automated testing, and drift detection; build dashboards and alerts to ensure reliability and data quality in production.
- Collaboration & Experimentation: Gather requirements, run experiments, and translate findings into actionable product enhancements for analytics, product, and editorial teams.
- Compliance: Apply GDPR/CCPA principles, enforce PII safeguards, and maintain documentation for audit readiness.
- Continuous Learning: Research emerging ML techniques, share best practices, and mentor junior engineers through code reviews and knowledge-sharing sessions.
Other
- Bachelor’s degree in Computer Science, Data Science, Mathematics, or related technical field.
- 3+ years of professional experience in machine learning engineering, delivering production-grade models or pipelines at scale.
- Strong collaboration skills with cross-functional teams and ability to translate technical findings into business insights.
- Travel requirements not specified
- Clearance requirements not specified
