Director of Data Services, Samba TV

• 8+ years of PM experience, with deep exposure to data infrastructure, ML platforms, or enterprise governance tooling
• Strong fluency in machine learning concepts, such as embeddings, training pipelines, model drift, and vector indexing
• Experience working with modern data stacks: Databricks, Delta Lake, Unity Catalog, and concepts like RBAC, schema enforcement, and metadata systems
• Proven success in leading cross-functional platform products with multiple stakeholder groups (data science, engineering, legal, external partners)
• Ability to influence highly technical teams while also driving stakeholder alignment across compliance and business functions
• Bonus: Experience at an adtech, martech, or audience data company, where data governance and modeling intersect with commercial product delivery

Desirable

• The Director of Data Services will be a pivotal business and product leader responsible for the entire data lakehouse layer of Samba TV's platform, establishing it as the central, unified data foundation for all product offerings
• This role is crucial for integrating and standardizing all Samba data and data policies, ensuring best-in-class data quality, accessibility, and implementation of governance
• This leader will define and execute the strategy for SambaTV’s internal data platforms
• Embeddings Platform: Design and scale the infrastructure that powers audience modeling, targeting, and measurement using high-dimensional vector representations of behavioral data
• Unity Catalog & Governance: Lead governance and data access strategy across teams by driving adoption and evolution of Unity Catalog, role based access controls (RBAC) frameworks, and lineage tracking
• Own the long-term roadmap for how Samba’s internal data and ML assets are made accessible, safe, and product-ready
• Partner with ML, infra, data platform, legal, and cross-functional product teams to create cohesive frameworks for access control, modeling, data discovery, and reusability
• Translate technical primitives (e.g., vector similarity search, data lineage) into product-level benefits for internal and external customers
• Drive adoption and best practices across internal teams by evangelizing platform capabilities while balancing compliance, scale, and usability
• Contribute to external partner enablement, especially where governance or ML modeling intersects with customer-facing solutions