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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 programmatic bid-time decisions.

• Design prompts and structured output schemas that help LLMs reason effectively about advertising metadata.

• Analyze and improve data quality — identify gaps in metadata coverage, propose enrichment strategies, and validate model outputs against ground truth.

• Collaborate with data scientists, analysts, and engineers to implement and operationalize models and algorithms.

• Identify and drive process improvements: automating manual processes, optimizing data delivery, improving model evaluation pipelines.

• Create and maintain comprehensive technical documentation.

• Mentor junior engineers and provide technical leadership on ML and data-related decisions.

• 4+ years in software engineering with a data science or ML focus.

• Engineering degree in Computer Science, Data Science, ML, Statistics, or related field.

• Strong 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.

Preferred skills :

• 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/B testing, deployment pipelines.

• Infrastructure-as-code (Terraform, CloudFormation).

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