Description

You will own the data science lifecycle for wireless network fault management, translating complex OSS/NMS alarm data into predictive maintenance insights.

Responsibilities

• Build and deploy machine learning models for alarm correlation, root cause analysis, anomaly detection, and predictive fault forecasting.
• Analyze large-scale time-series and event data from 2G/3G/4G/5G RAN and Core networks to identify recurring fault signatures.
• Develop automated pipelines to clean, normalize, and enrich alarm data from multiple sources including OSS, EMS, and CMDB.
• Create interactive dashboards and KPIs in Tableau, Power BI, or Grafana to track network health and operational trends.
• Collaborate with NOC and Network Engineering teams to translate analytical findings into actionable operational recommendations.

Required Skills

• 10+ years of experience in data science or analytics, with a strong focus on time-series data and event/alarm analytics.
• Proficiency in Python (Pandas, NumPy, Scikit-learn) or R for statistical analysis and model development.
• Experience with big data platforms including Spark, Hadoop, and Kafka for processing large-scale datasets.
• Strong SQL skills and familiarity with in-memory databases for efficient data retrieval and processing.
• Hands-on experience with visualization tools such as Tableau, Power BI, or Grafana.
• Deep understanding of machine learning algorithms for classification, clustering, and anomaly detection.
• Knowledge of wireless network architectures (RAN, Core) and IEN/IP/Ethernet networking concepts.
• Familiarity with OSS/NMS systems and network fault management processes.

Key Skills

Education

Any Graduate