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Director

Director, AI/ML & Data Science

Nielsen Sports·Posted 2 months ago

Location

All India

Experience

10–14 years

Required Skills

MLData ScienceNLPComputer VisionStatistical ModelingMachine LearningData MiningExperimental DesignSentiment AnalysisInformation ExtractionImage AnalysisObject DetectionAIMLOpsText AnalysisTopic ModelingChatbot DevelopmentImage ClassificationFacial RecognitionVideo UnderstandingWorkflow CreationText GenerationContent Summarization

About the Role

Role Overview:
As the Director of AI/ML & Data Science at Gracenote India, you will play a crucial role in leading and managing data science teams across various geographies. Your primary responsibilities will include establishing technical rigor, fostering a culture of collaboration and innovation, and delivering key business outcomes for Gracenote. You will be instrumental in driving initiatives in AI, core data science, NLP, computer vision, and MLOps.

Key Responsibilities:

  • • Manage multiple teams of data science and analytics engineers, providing both direct and indirect managerial oversight.

  • • Collaborate with local and global management to ensure the success and productivity of assigned teams.

  • • Cultivate a culture based on Nielsen values, emphasizing collaboration, innovation, and continuous learning.

  • • Establish a transparent operating rhythm to create a positive workplace environment with high retention rates.

  • • Track KPIs related to productivity and employee retention.

  • • Maintain a talent pipeline by establishing connections with universities and supporting internships and new joiner programs.

  • • Implement automation processes for recurring analyses and simulations to drive efficiency.

  • • Oversee the deployment and maintenance of machine learning models, AI solutions, and data pipelines.

  • • Enforce quality assurance processes to uphold the integrity and accuracy of audience measurement data.

  • • Drive research initiatives to enhance statistical sampling methodologies for panels and surveys.

  • • Lead the evolution of analytics to include predictive and prescriptive analytics in operational processes.

  • • Identify and implement market research methodologies relevant to the media measurement space.

  • • Represent the Global Data Solutions Data Science and Analytics team in cross-functional engagements.

  • • Stay updated on emerging trends and best practices in data science and media analytics.

  • • Lead projects in statistical modeling, machine learning, data mining, and experimental design.

  • • Develop and implement NLP solutions for text analysis, sentiment analysis, and chatbot development.

  • • Lead computer vision projects including image analysis, object detection, and facial recognition.

  • • Utilize GenAI tools for workflow creation and content summarization.
  • Qualifications:

  • • Bachelor's/Master's degree or PhD (preferred) in a quantitative research field.

  • • 10 years of experience in Data Science, AI, deep learning, and statistics.

  • • Demonstrated expertise in team building.

  • • 8-10 years of experience with statistical coding languages such as Python, Spark, SAS, R, Scala, and SQL. Role Overview:

  • As the Director of AI/ML & Data Science at Gracenote India, you will play a crucial role in leading and managing data science teams across various geographies. Your primary responsibilities will include establishing technical rigor, fostering a culture of collaboration and innovation, and delivering key business outcomes for Gracenote. You will be instrumental in driving initiatives in AI, core data science, NLP, computer vision, and MLOps.

    Key Responsibilities:

  • • Manage multiple teams of data science and analytics engineers, providing both direct and indirect managerial oversight.

  • • Collaborate with local and global management to ensure the success and productivity of assigned teams.

  • • Cultivate a culture based on Nielsen values, emphasizing collaboration, innovation, and continuous learning.

  • • Establish a transparent operating rhythm to create a positive workplace environment with high retention rates.

  • • Track KPIs related to productivity and employee retention.

  • • Maintain a talent pipeline by establishing connections with universities and supporting internships and new joiner programs.

  • • Implement automation processes for recurring analyses and simulations to drive efficiency.

  • • Oversee the deployment and maintenance of machine learning models, AI solutions, and data pipelines.

  • • Enforce quality assurance processes to uphold the integrity and accuracy of audience measurement data.

  • • Drive research initiatives to enhance statistical sampling methodologies for panels and surveys.

  • • Lead the evolution of analytics to include predictive and prescriptive analytics in operational processes.

  • • Identify and implement market research methodologies relevant to the media measurement space.

  • • Represent the Global Data Solutions Data Science and Analytics team in cross-functional engagements.

  • • Stay updated on emerging trends and best practices in data science and media analytics.

  • • Lead projects in statistical modeling, machine learning, data mining, and experimental design.

  • • Develop and implement NLP solutions for text analysis, sentiment analysis, and chatbot development.

  • • Lead computer vision projects including image analysis, object detection, and facial recognition.

  • • Utilize GenAI tools for workflow creation and content summarization.
  • Qualifications:

  • • Bachelor's/Master's degree or PhD (preferred) in a quantitative research f
  • HireIQ AI InsightsBeta

    Ideal Candidate

    Someone who has scaled a data science org from 5–15 people, shipped production ML/NLP/CV systems in media/analytics/AdTech, and transitioned from IC contributor to managing managers.

    Estimated Salary Range(medium confidence)

    20 L – ₹35 L per year

    Likely Interview Questions

    1. 1.Walk us through a time you built or restructured a data science team across multiple geographies—what was the biggest friction point and how did you resolve retention?
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