This role focuses on applying data science, statistics, machine learning, graph analytics, and KPI engineering to enable autonomous network intelligence. The AI Data Scientist will work with network telemetry, alarms, performance counters, inventory data, topology data, trouble tickets, service data, and digital twin models to develop analytical insights and predictive intelligence for autonomous network operations.
Key Responsibilities
- Analyse large-scale telecom network datasets across RAN, Core, IP, Transport, SD-WAN, Cloud, OSS, and service domains.
- Develop KPI engineering models for network performance, service quality, fault behaviour, customer impact, capacity, and resilience.
- Build statistical and machine learning models for anomaly detection, fault prediction, root-cause analysis, degradation detection, and proactive assurance.
- Develop data aggregation, cleansing, enrichment, and feature engineering pipelines for network telemetry and OSS data.
- Support digital twin analytics using topology, inventory, configuration, service dependency, performance, and fault data.
- Develop graph analytics models for network topology, entity relationships, dependency mapping, service impact, and fault propagation.
- Work with AI/LLM engineers to provide high-quality features, embeddings, metadata, and contextual datasets for RAG and agentic AI systems.
- Define network data quality rules, correlation logic, and entity resolution methods.
- Create reusable analytical models for RAN, Core, IP/MPLS, SD-WAN, fixed, and cloud network KPIs.
- Support AIOps use cases such as alarm reduction, incident prioritisation, predictive maintenance, and automated root-cause analysis.
- Work with OSS and inventory teams to align data models with TMF SID concepts and TMF Open API structures.
- Use BigQuery or equivalent analytics platforms to process large-scale network data.
- Ensure models are explainable, measurable, governed, and suitable for operational decision-making.
