The Director of AI/ML & Data Science Gracenote India will be central to the success of the data
science teams across geographies. This role is fundamental to the growth and success of
Gracenote AI & Data Science organization as a whole. Key goals of this role are to establish the
technical rigor, culture and operating rhythm of the organization, delivering the business outcomes
for Gracenote and working with local and global stakeholders to collaborate on key outcomes and
deliverables. This role will involve leading initiatives in AI, core data science, NLP, computer vision
and MLOps.
Responsibilities
- Manage multiple teams of Gracenote data science and analytics engineers, with direct and indirect managerial responsibilities.
- Work with local and global management across all relevant org partners to ensure success and productivity and business outcomes of assigned teams.
- Create a culture based on Nielsen values, fostering a culture of collaboration, innovation, and continuous learning and growth.
- Establish operating rhythm and cadence within areas of responsibility to ensure transparent communication and inclusiveness, with the aim of creating a positive high retention environment for associates.
- Track KPIs including productivity and employee retention.
- Coordinate and maintain a talent pipeline, including connections with Universities and supporting internships and new joiner programs.
- Continuously assess and improve business processes.
- Apply an automation mindset to automate processes for recurring analyses and simulations, focusing on efficiency.
- Oversee the deployment and maintenance of machine learning models, AI & LLM based solutions and data pipelines in a production environment.
- Establish and enforce rigorous quality assurance processes to maintain the integrity and accuracy of audience measurement data.
- Ensure adherence to tools of the Product Development Lifecycle.
- Drive research initiatives to improve existing methodologies for statistical sampling and research for panels and surveys.
- Oversee evolution of analytics to incorporate more predictive and prescriptive analytics into Nielsen’s operational processes.
- Identify and implement market research methodologies appropriate for the media measurement space.
- Represent Global Data Solutions Data Science and Analytics team in cross-functional (e.g., Product, Technology, Audience Measurement Data Science) engagements.
- Stay abreast of emerging trends, technologies, and best practices in data science, market research, and media analytics, contributing to thought leadership initiatives and industry forums.
- Lead initiatives in core data science, including statistical modeling, machine learning, data mining, and experimental design.
- Develop and implement NLP solutions for text analysis, sentiment analysis, topic modeling, information extraction, and chatbot development.
- Lead computer vision projects, including image/video analysis, object detection, image classification, facial recognition, and video understanding.
- Utilize GenAI tools for workflow creation, including building and deploying Agentic RAG systems, Small Language Models (SLMs), text generation, and content summarization.
- Bachelor’s/Master’s degree or PhD. (preferred) in a quantitative research field, such as computer science, data science, statistics, mathematics, biological/physical sciences, etc.
- 13+ years of experience working with Data Science, AI, deep learning and statistics.
- Proven track record in team building.
- 10+ years of experience with statistical coding languages such as Python, Spark, SAS, R, Scala, and/or SQL.
- 3-5 years of experience working in a cloud-based environment, ideally AWS.
- Good understanding of business tools such as JIRA, Airflow, Confluence, Smartsheets, Google Workspace suite.
- Excellent statistical and analytical skills.
- Experience in data integrations (e.g., developing data pipelines, analytics workflows, self-service apps)
- 3-5 years experience with low-code/no-code tools (e.g., Alteryx).
- 3-5 years experience in visualization tools e.g., Tableau, PowerBI, Looker, Spotfire, Salesforce dashboards.
- Excellent communication (verbal and written) and presentation skills in English.
- Excellent critical thinking and problem-solving skills.
- Sense of curiosity, skepticism, and attention to detail.
- Knowledge of market research methodologies, media consumption trends, and media industry regulations is a plus.
- Strong foundation in core data science principles, including statistical modeling, machine learning algorithms, and data manipulation techniques.
- Experience with NLP libraries and frameworks (e.g., NLTK, SpaCy, Hugging Face Transformers, Gensim).
- Experience with computer vision libraries and frameworks (e.g., OpenCV, TensorFlow,PyTorch, Keras).
- Hands-on experience with GenAI tools and techniques, including RAG, SLMs, prompt engineering, and fine-tuning pre-trained models.
- Familiarity with deploying and managing machine learning and GenAI models in production environments.
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