Data Scientist, Returnship (Path Forward Program)

Key Skills

Apache Airflow, Apache Hive, Data Modeling, Data pipelines, Data science techniques, Framework, Java Programming, Luigi, Machine learning techniques, Mathematics, Python Programming, Scikit-learn, SPARK Programming, SQL, Statistics

Job Description

The Path Forward Return to Work program at Disney Streaming is a 16-week paid returnship (internship) for experienced professionals returning to the workforce after taking time off for caregiving. The program is open to individuals who have at least 5 years of professional experience and have been out of the paid workforce for at least two years to focus on caring for a child or other dependent. If you meet these criteria, we welcome you to apply. 

At Disney Streaming we are excited to be part of the Path Forward program because we appreciate the skills you can offer, the perspective you provide, and the contributions you will make. This program offers you a chance to revamp your skills, update your resume with new experience, and make connections with others transitioning back to the workforce. It also offers support through Path Forward, a nonprofit organization on a mission to empower people who’ve been focused on caregiving transition back to the paid workforce.

SUMMARY

The Data Science team at Disney Streaming Services combines deep data analysis and research of our rich user data to present a compelling vision around user retention and preferences across the vast ecosystem of our product offerings and content. We are the insights and modeling partners for the content, marketing, product, and engineering teams at Disney+, Hulu and ESPN+. As Data Scientists, we use data to empower decision-makers with information, predictions, and insights that ultimately influence the experiences of millions of users worldwide. If you are someone who loves to work cross-functionally, build models, perform statistical analysis, and create visualizations to provide scalable and iterative capabilities, then this is a fantastic role for you!

WHAT YOU'LL DO

• Modeling: Apply state of the art machine learning and statistical analysis to develop models related to opportunity sizing, targeting, and optimization.
• Deep analysis: Analyze user behavioral data to identify patterns, uncover opportunities, and create common understanding of how people are interacting with the service and content.
• Visualization of Insights: Development of prototype solutions, mathematical models, algorithms, and robust analytics leading to impactful 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.

WHAT TO BRING

• Bachelors or Masters degree in a quantitative field (e.g. Computer Science, Engineering, Mathematics, Physics, Operations Research, Econometrics, Statistics)
• 2-4 years related experience with developing machine learning models and conducting statistical analysis
• Deep understanding of machine learning concepts and statistical methods
• Ability to draw insights and conclusions from data to inform model development and business decisions
• Desire to collaborate with other data scientists, data analysts, and key business partners
• Proficient in analyzing data and developing ML models using Python (with ML frameworks like tensorflow, scikit-learn, etc.)
• Capable of reading and writing Object-Oriented code to review and implement modeling pipelines (Airflow, Luigi, etc.)
• Understanding of SQL and distributed data technologies (Hive, Spark)

About Disney Media & Entertainment Distribution:

Comprised of the Company’s international business units and various direct-to-consumer streaming services, Disney Media & Entertainment Distribution (DMED) aligns technology, media distribution and advertising sales into a single business segment to create and deliver personalized entertainment experiences to consumers around the world.