The Nielsen Company
media
Sr.DataScientist/MemberTechnicalStaffII
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“Sr. Data Scientist / Member Technical Staff II at The Nielsen Company. Skills: Data Science, Machine Learning, Python, SQL, LLM/GenAI, Cloud platforms (AWS preferred). Design, build, and maintain the Ads MCP Server. Model and structure advertising data”
What You'll Achieve.
ship ML-powered features as real-time services consumed by AI agents via the Model Context Protocol (MCP); ensure segment lookups and metadata queries meet latency requirements for programmatic bid-time decisions
Industry & Context.
Analyze and improve data quality; Identify and drive process improvements
What They're Looking For.
Must Have
4+ years in software engineering with a data science or ML focus, Engineering degree in Computer Science, Data Science, ML, Statistics, or related field, Python — async patterns, type hints, data pipelines, ML fundamentals — classification, NLP, embeddings, similarity search, recommendations, model evaluation, Data modeling — complex metadata schemas, taxonomy design, structured/semi-structured data at scale, Expert SQL and relational databases, LLM/GenAI — integrating large language models, structured output, and prompt engineering, REST API design serving data and ML outputs, Cloud platforms (AWS preferred) and their AI/ML services, Git and CI/CD practices, Data quality, governance, and validation principles
Nice to Have
Model Context Protocol (MCP) or tool-use interfaces for AI agents, LangChain / LangGraph or similar agentic orchestration frameworks, AdTech domain — programmatic buying, OpenRTB, DSPs/SSPs, contextual targeting, Media metadata — content taxonomies (genre, mood, brand safety), Google Vertex AI / Gemini, AWS Bedrock, or similar cloud AI platforms, Vector databases / embedding retrieval, Big data (Spark, Flink) and pipeline tools (Airflow), Docker, Kubernetes, Real-time, low-latency data serving, MLOps — model versioning, monitoring, A testing, deployment pipelines, Infrastructure-as-code (Terraform, CloudFormation)
What You'll Do.
and maintain the Ads MCP Server
Model and structure advertising data
Develop ML-powered features
Integrate with agentic AI workflows
Optimize real-time data retrieval
Design prompts and structured output schemas that help LLMs reason effectively about advertising metadata
Analyze and improve data quality
Create and maintain comprehensive technical documentation
How You'll Work.
Team & Collaboration
Collaborate with data scientists, analysts, and engineers to implement and operationalize models and algorithms
Full Job Description
At Nielsen, we are passionate about our work to power a better media future for all people by providing powerful insights that drive client decisions and deliver extraordinary results. Our talented, global workforce is dedicated to capturing audience engagement with content — wherever and whenever it's consumed. Together, we are proudly rooted in our deep legacy as we stand at the forefront of the media revolution. When you join Nielsen, you will join a dynamic team committed to excellence, perseverance, and the ambition to make an impact together. We champion you, because when you succeed, we do too. We enable your best to power our future. We are seeking an experienced MTS 2 with a Data Science or Machine Learning background and 4+ years of experience to join our Ads AI team within Gracenote — a Nielsen Company. You will design and build intelligent data services that expose Gracenote's entertainment metadata to agentic AI systems for transparent, privacy-compliant CTV advertising targeting. Working at the intersection of data science and production engineering, you will ship ML-powered features as real-time services consumed by AI agents via the Model Context Protocol (MCP). Responsibilities * Design, build, and maintain the Ads MCP Server — exposing Gracenote's advertising metadata as structured tools and resources that AI agents can discover and invoke. * Model and structure advertising data — define schemas, taxonomies, and retrieval strategies that make content metadata (genre, mood, brand safety, program attributes) useful for agentic ad targeting workflows. * Develop ML-powered features — build models for content classification, segment recommendation, similarity matching, and contextual relevance scoring. * Integrate with agentic AI workflows — work with LangGraph/LangChain-based orchestration to enable multi-step reasoning over advertising data. * Optimize real-time data retrieval — ensure segment lookups and metadata queries meet latency requirements for
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