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San Francisco CA Full Time Posted: Monday, 11 February 2019
 
 
Applicants must be eligible to work in the specified location

Explore and Discover Nielsen! With offices located in 110 countries, we are a global independent measurement and big data analytics company focused on your future. Nielsen Portfolio is one of three core Nielsen businesses. The newly established Portfolio division is defined as a high-growth, innovation-based business that supports the needs of our clients. Portfolio includes Gracenote, Nielsen Sports/Esports/Games, Nielsen Music, Brandbank, Superdata, & Nielsen Book, making Nielsen Portfolio the largest supplier of metadata, data, measurement, and insights to the global Entertainment Industry.

Gracenote, a Nielsen Company is the leading provider of entertainment metadata and media recognition technology that powers discovery features for top TV, music, sports and automotive platforms.

We are presently looking for a Data Scientist for our Connectivity (Telecom) team who will be responsible for supporting custom analysis using data from existing Telecom products and assisting in the development of novel client solutions in the form of new products or combining data across Nielsen Telecom platforms.

This role can be based in San Francisco or Emeryville.

The Data Scientist for Telecom is responsible for supporting custom analysis using data from existing Telecom products and assisting in the development of novel client solutions in the form of new products or combining data across Nielsen platforms.

Job Responsibilities

  • Data Science product support (includes new feature development, methodology, client inquiry response)
  • Work across different products to help develop integrated vertical solutions for Connectivity
  • Work with the product leadership team to ensure compatibility of developing solutions with commercial need
  • Work with the Technology team to transform data streams into business insights
  • Coordinate with Operations team to transition completed solutions to production
  • Assist cross-functional teams in design, implementation, and testing of integrated product solutions.
  • Provide research support for the identification and implementation of methods and best practices to improve product quality.
  • Work across products when advanced/custom analysis is required for specific business opportunities.

Requirements

  • Experience or thorough training in Python
  • Strong SQL skills and relevant experience
  • Ability to manipulate/munge data, analyze, and interpret results of data analysis
  • Experience in big data technologies and AI/machine learning
  • B.S. or Master's degree in Data Science, Statistics, Social Science, Operation Research, or other hard sciences (eg, Engineering, Computer Science, Biology, Physics, etc.) with
  • analytical experience.
  • Experience in trend analyses, multivariate statistics, bias reduction, weighting
  • Data visualization experience (eg, Tableau, Spotfire, Microstrategy, Domo, etc.)
  • One to three years of relevant experience preferred
  • Strong written and verbal communication skills
  • Proficiency in SAS, SPSS, R, Matlab, or other statistical packages preferred

Gracenote, a Nielsen company, is committed to hiring and retaining a diverse workforce. We are proud to be an Equal Opportunity/Affirmative Action-Employer, making decisions without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability status, age, marital status, protected veteran status or any other protected class

Gracenote, a Nielsen company, is committed to hiring and retaining a diverse workforce. We are proud to be an Equal Opportunity/Affirmative Action-Employer, making decisions without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability status, age, marital status, protected veteran status or any other protected class


San Francisco CA, United States of America
IT
Nielsen
Nielsen
JS29199-EN_US
2/11/2019 8:20:50 PM


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