Description

You will analyze large-scale wireless network alarm data across RAN, Core, and IEN domains to identify patterns and recurring fault signatures. You will develop machine learning models for alarm correlation, noise reduction, and false-positive suppression, including root cause analysis and predictive fault forecasting. You will build interactive dashboards and reports for real-time fault monitoring, presenting analytical findings to technical and non-technical stakeholders. You will collaborate with NOC and Network Engineering teams to translate insights into actionable operational recommendations that reduce MTTR. You will automate data pipelines and model deployment processes using Python, R, and big data platforms.

Responsibilities

• Analyze alarm data from OSS, NMS, and EMS systems to identify trends and fault signatures.
• Develop ML models for anomaly detection, clustering, and time-series analysis.
• Build and maintain interactive dashboards using Tableau, Power BI, or Grafana.
• Automate data pipelines and model deployment workflows.
• Clean, normalize, and enrich data from multiple network sources.

Required Skills

• 5+ years of experience in data engineering or network analytics.
• Proficiency in Python and R with Pandas, NumPy, and Scikit-learn.
• Experience with PySpark, Spark, and Hadoop for big data processing.
• Ability to build visualizations using Tableau, Power BI, or Grafana.
• Understanding of wireless network technologies (2G-5G, RAN, Core, IEN).
• Strong SQL skills for querying and analyzing complex datasets.
• Experience with supervised and unsupervised learning techniques.

Preferred Skills

• Familiarity with OSS/NMS platforms and fault management processes.
• Experience with real-time streaming data technologies.

Key Skills

Education

Any Gradute