Define and lead the multi-year product strategy for Content Engineering & Intelligence across Paramount+ and Pluto TV
Turn company goals into scalable investments. Concentrate on metadata, content comprehension, embeddings, short-form systems, and content services.
Identify and develop new content capabilities, such as multimodal enrichment, automated tagging, and content graph evolution.
Represent Content Engineering & Intelligence strategy at the executive and cross-company level
Lead product strategy for content metadata architecture, modeling, and lifecycle management
Guide product direction for feature pipelines, enrichment workflows, canonical content data systems, and internal content services
Lead product planning for embedding generation, vector pipelines, and ML-ready content datasets
Partner with engineering and ML teams to build scalable, reliable, and cost-efficient content systems
Define and track north-star metrics for Content Engineering & Intelligence success
Requirements
10+ years of experience in product management, technical product management, or equivalent product executive team roles
5+ years leading technical product areas in content platforms, metadata systems, AI/ML infrastructure, content intelligence, data platforms, or adjacent domains
Experience leading PMs or complex technical product areas across engineering, ML, data, design, operations, and business teams
Have experience in delivering large content platforms. This includes metadata systems, internal tools, data platforms, and machine learning systems.
Technical fluency across data systems, content pipelines, APIs, internal tools, metadata systems, and ML-enabled platforms
Experience with content metadata systems. Knowledge of enrichment workflows and canonical metadata models.
Knowledge of content identifiers, taxonomy, ontology, and content normalization methods.
Experience with data pipelines.
Experience with feature stores.
Experience with datasets that are ready for models.
Experience with batch and near-real-time architectures.
Knowledge of MLOps, monitoring, and experimentation systems.
Executive communication skills.
Ability to align engineering, ML, design, operations, product, and business stakeholders across matrixed organizations.