Senior Specialist Product Owner

Main Responsibilities

• Own the end-to-end product lifecycle for data-driven features and models—from ideation through development, deployment, and iteration.
• Design and oversee the development of scalable data models, with a focus on network data standards such as IETF YANG.
• Translate business needs into clear data product requirements, ensuring alignment between stakeholders, technical teams, and strategic goals.
• Define and prioritize product roadmaps, user stories, and backlogs in collaboration with engineering, data science, and business teams.
• Develop and support machine learning pipelines for tasks like anomaly detection, predictive maintenance, and performance optimization.
• Apply statistical and analytical methods (e.g., regression, clustering, outlier detection) to extract insights and support business decisions.
• Collaborate with cross-functional teams, including data engineers, ML researchers, domain experts, and executives, to deliver impactful solutions.
• Validate and measure the success of data initiatives using relevant KPIs, A/B testing, and performance tracking frameworks.
• Ensure data governance, quality, and compliance throughout the modeling and product development process.
• Act as a subject matter expert on data modeling and ML applications within cloud off the shelf solutions and complex network infrastructures.
• Develop and maintain YANG models for telemetry and configuration data from satellite and terrestrial networks.
• Design data models to support anomaly detection, predictive analytics, and network optimization.
• Use data modeling tools to extract insights from high-volume telemetry data.
• Build real-time and batch data pipelines using Apache Kafka, Spark, and Google Dataflow.
• Deploy ML pipelines in cloud and hybrid environments.
• Collaborate with business stakeholders to gather requirements, define KPIs, and align data initiatives with strategic goals.
• Translate complex technical findings into clear business insights and visualizations.
• Support data governance, lineage, and metadata integration across the data lifecycle

Required Qualifications

• Bachelor’s or master’s degree in computer science, Data Science, Engineering, Applied Mathematics, or a related technical field.
• 5+ years of experience in data science, data modeling, or cloud-based machine learning, with at least 2 years in a product owner or technical leadership role.
• Strong experience in network data modeling, especially using IETF YANG or similar data modeling languages.
• Proficient in machine learning frameworks and in building ML pipelines in production environments.
• Hands-on experience with statistical methods such as regression, classification, clustering, and outlier detection.
• Familiarity with cloud platforms and data engineering tools (e.g., Spark, Kafka, Airflow).
• Demonstrated ability to translate business needs into technical requirements, and to manage a backlog and product roadmap.
• Excellent communication and stakeholder management skills, with experience working cross-functionally between technical and non-technical teams.
• Experience with Agile methodologies, including sprint planning, backlog grooming, and user story definition.
• Strong understanding of data governance, privacy, and security best practices in complex or regulated environments.
• Experience defining or contributing to organization-wide AI strategy or governance frameworks.
• Knowledge of AI/ML infrastructure at scale, including MLOps tools and model monitoring strategies.
• Experience managing AI use case prioritization across multiple business domains.
• Experience with NETCONF, RESTCONF, or gNMI. (Preferred)
• Knowledge of event-driven architectures and real-time analytics.