Company Description NBCUniversal is one of the world's leading media and entertainment companies. We create world-class content, which we distribute across our portfolio of film, television, and streaming, and bring to life through our global theme park destinations, consumer products, and experiences. We own and operate leading entertainment and news brands, including NBC, NBC News, NBC Sports, Telemundo, NBC Local Stations, Bravo, and Peacock, our premium ad-supported streaming service. We produce and distribute premier filmed entertainment and programming through our powerhouse film and television studios, including Universal Pictures, DreamWorks Animation, and Focus Features, and the four global television studios under the Universal Studio Group banner, and operate industry-leading theme parks and experiences around the world through Universal Destinations & Experiences, including Universal Orlando Resort, home to Universal Epic Universe, and Universal Studios Hollywood. NBCUniversal is a subsidiary of Comcast Corporation. Visit www.nbcuniversal.com for more information. Our impact is rooted in improving the communities where our employees, customers, and audiences live and work. We have a rich tradition of giving back and ensuring our employees have the opportunity to serve their communities. We champion an inclusive culture and strive to attract and develop a talented workforce to create and deliver a wide range of content reflecting our world. Job Description As Lead Product Manager in Product Management - Data Analytics, you will define and execute the vision, strategy, and roadmap for analytics and data products that power decision-making and performance across Ad Sales. This role requires a hands-on, highly technical product leader who is fluent in data engineering concepts, relational modeling, AI/ML applications, and enterprise analytics platforms. You will translate complex business needs into scalable data products, collaborating closely with data engineering, platform teams, and business stakeholders to transform multi-source datasets into actionable, self-service insights. Key Responsibilities Product Strategy & Roadmap - Define and socialize a long-term strategy and outcome-based roadmap for analytics and data products aligned with Ad Sales priorities and NBCU technology standards. - Explore and evaluate internal/third-party datasets; shape analytical tools, AI-enabled insights, and scalable data products that deliver measurable business impact. - Drive innovation by identifying emerging technologies, data patterns, and opportunities to improve decisioning, automation, and predictive intelligence across the Ad Sales ecosystem. Solution Delivery - Translate business needs into clear, technically sound product requirements, functional specifications, user stories, KPIs, and measurable success criteria. - Work hands-on with data-querying datasets, validating logic, understanding source-to-target mappings, entity relationships, and data transformation flows. - Lead discovery and prototyping initiatives to validate concepts; partner with architecture, platform, and data engineering teams to build and deploy data pipelines, AI/ML models, and BI products. - Ensure quality, reliability, and scalability across ingestion, modeling, orchestration, and visualization layers. Program & Project Leadership - Plan and manage execution for complex cross-functional initiatives; identify, track, and communicate milestones, dependencies, and risks across globally distributed teams. - Establish governance frameworks for backlog management, prioritization, release planning, and change management. - Uphold enterprise data governance, privacy, and security standards throughout the product lifecycle. Stakeholder Engagement & Adoption - Build strong partnerships with Sales Operations, Strategy, Finance, Marketing, and Engineering; translate user needs into product capabilities and ensure successful adoption via self-service enablement. - Communicate strategy, progress, and business outcomes to leadership through compelling storytelling and data-driven insights. - Drive continuous improvement by synthesizing feedback, telemetry, usage analytics, and performance trends. Team Leadership - Coach and mentor product managers, analysts, and technical team members; foster a collaborative, experimentation-oriented culture focused on excellence and accountability. - Create alignment across engineering, business stakeholders, and leadership around vision, priorities, and delivery expectations. Qualifications - 12+ years in Technical Product Management with deep experience delivering enterprise data, analytics, or BI products end-to-end. - Demonstrated strengths across core product pillars: Business Strategy Alignment, Solution Delivery, Requirements Definition, and Project Execution. - 7+ years of hands-on experience with modern data stacks-including Snowflake, Databricks, Redshift, BigQuery, AWS/Azure, dbt, and distributed computing frameworks. - Strong SQL skills with the ability to query, validate, and analyze large datasets; familiarity with data modeling techniques (Star/Snowflake schema, dimensional modeling, entity-relationship design). - Experience building scalable data products and BI solutions using tools such as Tableau, Power BI, MicroStrategy, Looker, or similar platforms. - Familiarity with AI/ML workflows (feature engineering, model evaluation, inference pipelines) and pragmatic application of AI in analytics products. - Experience in Ad Tech and Ad Sales ecosystems including identity, audience data, measurement, forecasting, and campaign analytics. - Proven ability to lead cross-functional teams, influence executives, and communicate complex technical concepts to business stakeholders. - Strong understanding of data governance, data quality, privacy, and compliance (GDPR, CCPA) and ability to operationalize guardrails in product workflows. - Ability to "hold your own" in technical discussions with architects, engineers, and data scientists. Preferred - Experience with Adobe Analytics, Google Ad Manager/DoubleClick, FreeWheel, Nielsen, Comscore, LiveRamp, or DoubleVerify. - PMP, Scrum Master certification, or MBA. - Track record of leading global teams and shipping enterprise-scale data or AI-powered products. - Knowledge of Agile methodologies and JIRA workflows. - Passion for data storytelling, comfort navigating complex relational datasets, and the ability to articulate insights using data. Additional Requirements: - Hybrid: This position has been designated as hybrid, which currently requires contributing from the office a minimum of four days per week. The Company reserves the right to change in-office requirements at any time. This position is eligible for company sponsored benefits, including medical, dental and vision insurance, 401(k), paid leave, tuition reimbursement, and a variety of other discounts and perks. Learn more about the benefits offered by NBCUniversal by visiting the Benefits page of the Careers website. Salary Range: $150,000 - $190,000 (bonus eligible) Additional Information As part of our selection process, external candidates may be required to attend an in-person interview with an NBCUniversal employee at one of our locations prior to a hiring decision. NBCUniversal's policy is to provide equal employment opportunities to all applicants and employees without regard to race, color, religion, creed, gender, gender identity or expression, age, national origin or ancestry, citizenship, disability, sexual orientation, marital status, pregnancy, veteran status, membership in the uniformed services, genetic information, or any other basis protected by applicable law. If you are a qualified individual with a disability or a disabled veteran and require support throughout the application and/or recruitment process as a result of your disability, you have the right to request a reasonable accommodation. You can submit your request to AccessibilitySupport@nbcuni.com.

• Work closely with executives, sponsors, analyst teams, and engineering teams to create incredible data products on time, aligned with strategic vision, and business needs. • Communicate data product and user experience definitions, enabling analytics and engineering teams to hit the ground running on new data product and feature development. • Own the strategic vision for the data platform, prioritizing investments that create durable, scalable capabilities that enable distributed domain ownership of data products. • Develop strategies to support specification, rapid prototyping, release management, and quality assurance for data products. • Facilitate the resolution of conflicts between domains regarding bounded concepts or metric definitions.

• Drive the lifecycle of government-centric products, from conception to deployment, ensuring alignment with federal standards and agency-specific requirements. • Organize and facilitate scrum ceremonies, including daily stand-ups, grooming sessions, retrospectives, sprint and release planning and demos • Ensure the development teams are practicing the core agile principles of collaboration, team accountability, and ensuring the work is visible. • Remove impediments and coach the scrum team on removing impediments • Supervise staff in day-to-day activities including planning, organizing work, and adjusting workloads • Serve as the primary point of contact for government stakeholders, communicating product objectives, compliance strategies, and timelines effectively. • Oversee adherence to federal regulatory requirements, identifying and mitigating risks associated with system authorization and ongoing security postures. • Develop and maintain performance indicators for sustainment and product adoption, ensuring continuous improvement and stakeholder satisfaction. • Coordinate with contracting teams to ensure product delivery aligns with agency missions, contract requirements, and performance obligations. • Stay abreast of emerging technologies and policies in the federal landscape to ensure product relevance and competitiveness. • Other responsibilities as assigned.

The AI Agent Manager, Commercial is responsible for identifying, designing, building, and deploying AI and automation agents across the sales and revenue operations function. This is a hands on, senior individual contributor role focused on driving bottom up adoption of AI powered workflows within commercial teams. The role exists to accelerate sales productivity, improve pipeline quality, reduce manual effort in quoting, proposal generation, and customer communications, and embed a culture of AI augmented working across the commercial organisation. The AI Agent Manager works closely with sales leaders, revenue operations, and product teams to identify high value use cases, prototype solutions, measure adoption, and scale what works. Responsibilities 1. Use Case Discovery and Prioritisation: Identify and evaluate high value opportunities to deploy AI agents across the sales function, including lead qualification, outbound sequencing, proposal drafting, quote generation, and CRM data enrichment. Prioritise based on effort, impact, and adoption readiness. 2. Agent Design, Build, and Deployment: Design, configure, and deploy AI agents and automated workflows using LLMs, APIs, and integration tools. Own the end to end lifecycle from prototype through production deployment, including testing, monitoring, and iteration. 3. Sales Productivity Improvement: Reduce manual effort in repetitive commercial workflows by embedding AI agents into daily sales activities. Measure time saved, error reduction, and throughput improvements. Ensure agents augment rather than replace human judgment in customer facing interactions. 4. Adoption and Change Management: Drive bottom up adoption by working directly with sales teams and individual contributors. Provide hands on training, create playbooks, run workshops, and build internal champions. Track adoption metrics and iterate based on user feedback. 5. Data Quality and Integration: Ensure AI agents operate on clean, reliable data by working with revenue operations and data teams. Define data requirements, build integration points with CRM, ERP, and communication tools, and establish feedback loops to improve agent accuracy over time. 6. Performance Measurement and Reporting: Define and track KPIs for each deployed agent including adoption rates, productivity gains, accuracy, and ROI. Report outcomes to commercial leadership and use evidence to inform scaling decisions and investment cases. 7.  Cross Functional Collaboration: Partner with Product, Engineering, and IT to align agent deployments with platform capabilities, security requirements, and data governance standards. Ensure agents are built within approved frameworks and comply with company policies. 8. Experimentation and Innovation: Stay current with emerging AI capabilities, LLM developments, and automation tools. Run structured experiments to test new approaches and share learnings across the organisation to build collective AI fluency. 9.  Documentation and Knowledge Management: Maintain clear documentation for all deployed agents including design rationale, configuration, dependencies, known limitations, and escalation paths. Build a reusable library of agent patterns and templates. 10. Vendor and Tool Evaluation: Evaluate and recommend AI tools, platforms, and vendors relevant to commercial use cases. Provide informed build versus buy recommendations within group guardrails.