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Hulu Jobs – Lead Data Scientist In Rosemead

    Website Hulu

    Job Description:

    Data scientists at Disney Streaming Services 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.

    Job Responsibilities:

    • Visualization of Complex Data sets: Development of prototype solutions, mathematical models, algorithms, and robust analytics leading to actionable insights communicated clearly and visually.
    • 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.
    • 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.
    • 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.

    Job Requirements/Qualifications:

    • Master’s degree in a quantitative field (e.g. Computer Science, Engineering, Mathematics, Physics, Operations
    • Research, Econometrics, Statistics)
    • 7+ years of experience with statistical programming language (e.g. Python, Spark, PySpark) and database languages (e.g. SQL)
    • 7+ years of experience designing, building, and evaluating practical machine learning solutions

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