Gracenote, a Nielsen company, is dedicated to connecting audiences to the entertainment they love, powering a better media future for all people. Gracenote is the content data business unit of Nielsen that powers innovative entertainment experiences for the world’s leading media companies. Our entertainment metadata and connected IDs deliver advanced content navigation and discovery to connect consumers to the content they love and discover new ones. Gracenote’s industry-leading datasets cover TV programs, movies, sports, music and podcasts in 80 countries and 35 languages. Gracenote provides common identifiers that are universally adopted by the world’s leading media companies enabling powerful cross-media entertainment experiences. Machine driven, human validated best-in-class data and images fuel new search and discovery experiences across every screen. Gracenote's Data Organization is a dynamic and innovative group that is essential in delivering business outcomes through data, insights, predictive & prescriptive analytics. An extremely motivated team that values creativity, experimentation through continuous learning in an agile and collaborative manner. The data team oversees the whole data lifecycle – from designing, developing and maintaining data architecture that satisfies our business goals to managing data governance and region-specific regulations. Role Overview: As a Lead Data Science Engineer on the Gracenote Media team, you will be responsible for defining the AI/ML strategy, overseeing large-scale data science projects, and leading teams to build cutting-edge machine learning solutions that scale content understanding and generation, entity linkage, and more to achieve the scale that matches our customer’s demands.
Key Responsibilities:
Define and execute the AI/ML strategy for content generation (Gen-AI), entity linkage and matching, image processing, and content understanding.
Lead the development of next-generation content generation systems using Large Language Models (LLMs)
Architect scalable data platforms to support real-time and batch processing of media-rich datasets.
Own the Data Quality of the deliverables and ensure the consistent performance of the models and its output quality.
Define and implement standards for the organization that match industry best practices.
Collaborate with product, engineering, and business teams to drive AI-powered innovation.
Improve computer vision models for automated content tagging, video summarization, and understanding.
Oversee MLOps infrastructure to ensure robust deployment and monitoring of ML models.
Stay ahead of emerging trends in AI, deep learning, and media tech, integrating new research into practical applications.
Mentor and grow a team of data scientists and engineers.
Required Skills:
Expert-level proficiency in Python, SQL, and big data tools (Spark, Kafka, Airflow).
Extensive experience in deep learning, reinforcement learning, NLP, and computer vision.
Experience in large-scale machine learning model deployment and optimization.
Proven leadership skills in building and scaling data science teams.
Experience with Kubernetes, Docker, and cloud AI services.
Qualifications:
Master’s in AI, Machine Learning, Data Science, or a related field.
8+ years of experience in data science and machine learning, with at least 3+ years leading teams.
Strong track record in building AI-driven solutions for media and entertainment.
