Senior Software Engineer, Tools and Automation - Video Platform
New York, NY US
Comprised of Disney's international media businesses and the Company's various streaming services, the Direct-to-Consumer and International (DTCI) segment aligns technology, content and distribution platforms to expand the Company's global footprint and deliver world-class, personalized entertainment experiences to consumers around the world.
The Walt Disney Company's Direct-to-Consumer and International segment (DTCI) is a global, multiplatform media, technology and distribution organization for high-quality content created by Disney's Studio Entertainment and Media Networks groups.
DTCI includes Disney's international media operations and the Company's direct-to-consumer businesses globally, including the upcoming Disney-branded direct-to-consumer streaming service, the Company's ownership stake in Hulu, and the ESPN+ sports streaming service, programmed in partnership with ESPN. BAMTECH Media, developer of the ESPN+ and Disney-branded streaming platforms, oversees all consumer-facing digital technology and products across the Company as part of the Direct-to-Consumer and International segment.
Data Scientists in the Disney+ Personalization team specialize in applying machine learning methods to meet strategic product personalization goals, explore innovative techniques that can be applied to recommendations and constantly seek ways to optimize operational processes. As a member of this team you will collaborate across Data, Product and Engineering teams to deeply understand challenges and develop automated solutions to be built into our products.
• Model creation and algorithm development: Create models from available data sources (e.g., metadata, behavioral, explicit, etc.) to advance and optimize personalized recommendations. Use state-of-the-art machine learning and deep learning methods to develop algorithms using these models to score and predict the optimal mix of content at the most granular user levels.
• Data Preparation: Process, cleanse and verify the integrity of data used for model building, experimentation evaluation and algorithm performance
• Analysis: Perform deep dive analysis on app interactions and events to better understand user consumption behavior as it relates to personalization and recommendation.
• Partnership: Collaborate with business stakeholders to help identify and define personalization opportunities. Work with other data teams to improve our data collection, experimentation and analysis.
• 5+ years of analytical experience
• 3+ years of work experience using SQL
• 3+ years writing production-level, scalable code (Python/Scala/C++)
• Experience with Python/R or other statistical programming languages
• Experience with cloud services (particularly AWS)
• Experience with advanced data modeling and predictive analytics
• In-depth understanding of modern machine learning (including deep learning, reinforcement learning) models and their mathematical underpinnings
• Experience with deep learning, NLP, Neural Networks, and/or Bayesian modeling
• Familiarity with data exploration and data visualization tools like Tableau, Looker, Chartio, etc.
• Understanding of statistics concepts (e.g., hypothesis testing, regression analysis)
• Ability to gauge the complexity of machine learning problems and a willingness to execute simple approaches for quick effective solutions as appropriate
• Strong communication skills, as well as written and verbal presentation skills
• Explain how models are used and algorithms behave to both technical and non-technical audiences
• Experience building and deploying full stack ML pipelines: data extraction, data mining, model training, feature development, regression testing, testing and deployment
• Experience with graph-based data workflows such as Apache Airflow
• Experience engineering big-data solutions using technologies like EMR, S3, Spark, Databricks
• Familiar with metadata management, data lineage, and principles of data governance
• Experience loading and querying cloud-hosted databases such as Snowflake
• Familiarity with automated deployment, AWS infrastructure, Docker or similar containers
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