Autonomous Network AI Data Scientist: KPI & Graph Analytics

Capgemini; Abingdon; Hybrid; GBP 70,000 - 110,000; Full time

Benefits offered by this job

Hybrid working model

Travel to client site

Job summary

Capgemini is seeking an AI/Data Scientist to apply data science, statistics, and ML to autonomous network intelligence. You will work with network telemetry, topology, and service data to build predictive insights and KPI models across RAN, Core, IP/MPLS, SD-WAN, and cloud domains.

You will collaborate with AI/LLM engineers, develop data pipelines, and ensure explainable, governed models for operational decision-making in a hybrid Newbury-based role.

Qualifications

• Experience in data science, telecom 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 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.

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.

Skills

Python

Statistics

ML basics

KPI engineering

AIOps analytics

Data pipelines

Graph analytics

LLM understanding

BigQuery

Network KPI modelling

Tools

BigQuery

Graph APIs

Open API

TMF SID

Job description

Capgemini is seeking an AI/Data Scientist to apply data science, statistics, and ML to autonomous network intelligence. You will work with network telemetry, topology, and service data to build predictive insights and KPI models across RAN, Core, IP/MPLS, SD-WAN, and cloud domains.

You will collaborate with AI/LLM engineers, develop data pipelines, and ensure explainable, governed models for operational decision-making in a hybrid Newbury-based role.