AI Data Scientist - Autonomous Network

• Experience in data science, machine learning, telecom analytics, network performance analytics, or AIOps analytics.
• Strong Python skills for data analysis, modelling, and automation.
• Strong knowledge of statistics, ML basics, feature engineering, and model evaluation.
• Experience working with network KPIs, alarms, telemetry, inventory, topology, or service assurance data.
• Understanding of telecom network domains including RAN, Core, IP/MPLS, SD-WAN, Transport, Cloud, or OSS.
• Experience developing anomaly detection, fault analysis, predictive analytics, or KPI models.
• Experience with data aggregation, cleansing, enrichment, and data quality assessment.
• Experience with graph analytics, entity relationship mapping, or knowledge graph concepts.
• Understanding of LLMs and how structured data can support AI agents and RAG systems.
• Experience with BigQuery or similar data warehouse/analytics platforms.
• Python.
• Statistics and ML basics.
• Network KPI modelling.
• RAN, Core, IP, SD-WAN, and Transport KPI understanding.
• Fault analysis and anomaly detection.
• AIOps analytics.
• Data aggregation and data pipelines.
• KPI engineering and feature engineering.
• Inventory models.
• Fault correlation.
• TMF SID understanding.
• BigQuery or equivalent analytics platform.
• Graph APIs and graph analytics.
• Digital twin analytics.
• LLM understanding.

• Google Cloud Data Engineer or Machine Learning Engineer.
• Azure Data Scientist or AWS Machine Learning certification.
• Databricks, Snowflake, or equivalent data platform certification.
• TM Forum SID, Open API, or Autonomous Networks training.

• Experience with telecom digital twin platforms.
• Experience with vector databases, embeddings, semantic search, or RAG.
• Experience with streaming data platforms such as Kafka, Pub/Sub, Flink, or Spark Streaming.
• Experience supporting closed-loop automation, predictive assurance, or self-healing network use cases.
• Experience working with OSS systems, inventory platforms, assurance systems, and ticketing data.

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.

• 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.

• All roles will require a level of security clearance; BPSS OR Security Clearance OR Developed Vetting.

• Location: This is a permanent role with Capgemini, offering a hybrid working model. The client is based in Newbury and occasional travel to the client site will be required.

• You can bring your whole self to work. At Capgemini building an inclusivefuture is part of everyday life and will be part of your working reality. We havebuilt a representative and welcoming environment, for everyone.