Disney Media & Entertainment Distribution | Data Scientist I | New York, NY | United States | BigDataKB.com | 17 Oct 2022

Disney Streaming Services is responsible for developing and operating The Walt Disney Company’s direct-to-consumer video businesses globally, including the ESPN+ premium sports streaming service; Disney+, the dedicated streaming home for entertainment from Disney, Pixar, Marvel, Star Wars, National Geographic and BAMTECH Media, a global leader in direct-to-consumer video streaming products and solutions. Our core mission is to deliver global audiences the freedom to access content on their terms across any connected device, time or location. We serve consumers by bringing the world’s most beloved characters, timeless stories, legendary athletes, and epic sporting events to global audiences through best-in-class direct-to-consumer video services. We strive daily to imaginatively challenge convention with innovative technology that gives consumers the freedom to access content on their terms across any connected device, time or location.

Data scientists at Disney are the insights and modeling partners for the growth, content, marketing, product, and engineering teams at Disney+, Hulu and ESPN+. They use data to empower decision-makers with information, predictions, and insights that ultimately influence the experiences of millions of users worldwide. Scientists on the team build models, perform statistical analysis, and create visualizations to provide scalable, persistent capability that is iteratively improved through direct interaction with cross-functional business partners.

As a data scientist in the Core Data Science team, you will be partnering closely with the Analytics team, Finance, Business Operations, Commerce Product and Engineering teams to develop machine learning models and end-to-end data solutions for tackling a multitude of exciting challenges, including customer lifetime value modeling, signups and subscribers forecasting, fraud modeling, commerce optimization, and more.

We look for someone with analytical and modeling expertise, a proven track record of thought leadership and eagerness to drive impact.

Responsibilities

• Modeling: Design, build and improve machine learning models. Work end to end from data collection, feature generation and selection, algorithm development, forecasting, visualization and communicating of model results. Collaborate with engineering to productionize models. Drive experimentation to test impact of model based optimization.
• Deep analysis: Develop comprehensive understanding of subscriber and payment data structures and metrics. Mine large data sets to identify opportunities for driving growth and retention of subscribers.
• Visualization of Complex Data sets: Development of prototype solutions, mathematical models, algorithms, and robust analytics leading to actionable insights communicated clearly and visually.
• Partnership: Partner closely with business stakeholders to identify and unlock opportunities, and with other data teams to improve platform capabilities around data modeling, data visualization, experimentation and data architecture.

Basic Qualifications

• Bachelor’s degree in a quantitative field (e.g. Computer Science, Engineering, Mathematics, Physics, Operations Research, Econometrics, Statistics)

Preferred Qualifications

• Advanced degree (M.S. or Ph.D.) in a quantitative discipline
• 1+ years of experience designing, building, and evaluating practical machine learning solutions
• 1+ years of experience with statistical programming language (e.g. Python, Spark, PySpark) and database languages (e.g. SQL)
• Excellent analytical skills, advanced level of statistics knowledge
• Strong expertise with Python and libraries such as scikit-learn, scipy
• Familiarity with Bayesian modeling and probabilistic programming packages such as PyMC
• Familiarity with data platforms and applications such as Databricks, Jupyter, Snowflake, Airflow, Github
• Familiarity with data exploration and data visualization tools such as Tableau, Looker
• Familiarity with designing and analyzing A/B testing and other experiment types
• Demonstrated skills in selecting the right statistical tools given a data analysis problem
• Ability to adapt quickly in a fast-moving environment with shifting priorities
• Strong communication skills, for both technical and non-technical audiences
• Ability to handle multiple tasks concurrently and in a timely manner, including large and complex ones
• Demonstrated leadership experience, including people and project management