Sony Music Entertainment

  • Data Scientist

    Location US-NY-New York
    Posted Date 2 weeks ago(11/29/2018 4:59 PM)
    Job ID
    # Positions
    Digital Business - Other
  • Overview

    As part of the Data Strategy team, the ideal Data Scientist will be a global internal expert on leveraging data to optimize current business priorities and to develop a strategic roadmap. Responsibilities will include, but are not limited to the execution of data analyses to support business decisions, establish best practices in analysis of data, statistical methods, and data mining, designing complex algorithms and statistical predictive models.

    This role will report to the EVP of Data Strategy, Chief Data Officer, and develop training for both the data team and business functions that work closely with the team. This role is based at Sony Music’s offices in New York.

    Sony Music is committed to providing equal employment opportunity for all persons regardless of age, disability, national origin, race, color, religion, sex, sexual orientation, gender identity or expression, pregnancy, veteran or military status, genetic information or any other status protected by applicable federal, state, or local law.



    • Determine analytical approaches and modeling techniques to evaluate scenarios and potential future outcomes.
    • Oversee and facilitate the use of internal tools and the use of large sets of data received from partners.
    • Process and verify the integrity of data used for analysis.
    • Ongoing research on innovations in the data science industry to continuously grow and develop the SME data strategy function.


    • Proficient in: Data Mining, Machine Learning, Python/R, Spark, SQL, Predictive Analytics, Data Visualization, Statistics, Data Engineering, Cloud Computing.
    • 2+ years of experience in data science.
    • Master’s Degree in a related field required.
    • Proficient in: Python/Jupytier Notebook, Pandas, Numpy, Pyspark/Databricks, Sci-Kit Learn/Spark ML, Linear regression models, logistics regression models, SVM/SVC, Random Forrest, K-Means, PCA, Mixture Models, Text analysis.


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