Data Science

Product Owner – Data Science & Network Modelling The team at Myticas Consulting is seeking a Product Owner – Data Science & Network Modelling with deep expertise in network data modelling (especially IETF YANG), machine learning, and business analysis. This role is ideal for someone who can translate complex technical challenges into actionable product strategies, enabling data-driven decision-making across satellite and ground network systems. You will design scalable data models and machine learning pipelines, apply ML techniques in cloud environments, and work closely with stakeholders to prioritize features and align product development with business needs. As the key link between technical teams and strategic objectives, you’ll define and own the roadmap for data-centric solutions that drive operational efficiency and innovation. 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. #J-18808-Ljbffr