Troubleshooting – Anomaly Detection

Requirements:

• Have intermediate to strong experience in anomaly detection and data analysis, preferably within the telecommunications/network domain.
• Able to identify abnormal behavior and deviations in network KPI and customer QoE data.
• Experienced in detecting outliers, anomalies, and unusual patterns across large and complex datasets.
• Able to analyze temporal patterns to identify abnormal behavior across hourly, daily, weekly, or other relevant time periods.
• Able to analyze geographic patterns to identify unusual performance or QoE behavior across regions, sites, cells, clusters, or other locations.
• Able to distinguish between normal fluctuations and abnormal network behavior using historical data, thresholds, baselines, or statistical patterns.
• Familiar with analyzing KPIs such as availability, accessibility, retainability, throughput, latency, traffic, and QoE indicators.
• Able to correlate anomalies across multiple data sources, including PM/KPI, CDR/xDR, OSS/NMS, traffic, and customer experience data.
• Able to investigate potential causes and identify patterns or correlations associated with detected anomalies.
• Familiar with basic statistical techniques for anomaly detection, such as threshold analysis, deviation analysis, distribution analysis, and baseline comparison.
• Familiar with visualization tools to identify trends, outliers, and abnormal patterns.
• Proficient in SQL and familiar with Python/Pandas for data analysis and anomaly identification.
• Able to document and communicate anomaly findings, affected areas/periods, and supporting data evidence clearly.
• Strong analytical thinking and problem-solving skills with attention to detail.

Key Competency:

Anomaly Detection — Intermediate–Strong ability to identify and investigate abnormal KPI/QoE behavior, outliers, and unusual geographic or temporal patterns, using historical baselines, statistical analysis, and multi-source network data correlation.