Senior Data Analyst, Network Analytics & AI

Required Qualifications

• Bachelor's or Master's in Computer Science, Statistics, Engineering or a related field.
• 10+ years of experience, 5+ years in data analytics, including at least 2 in a senior or lead role.
• Strong SQL and Python (pandas, NumPy); comfortable with large, high-volume datasets.
• Solid grasp of statistics, time-series analysis and anomaly detection.
• Hands-on experience with ML libraries such as scikit-learn, XGBoost or statsmodels.
• Practical knowledge of time-series forecasting (Prophet, ARIMA or similar) and unsupervised anomaly detection (Isolation Forest, clustering).
• Understanding of model evaluation, feature engineering and how to avoid common problems like data leakage and class imbalance.
  
  

Preferred Qualifications



• Experience with the ELK stack (Elasticsearch, Logstash, Kibana) and Kafka.
• Telecom domain knowledge (RAN, 4G/5G KPIs, O-RAN).
• Familiarity with Kubernetes, cloud platforms (AWS) or data engineering workflows.
• Experience applying LLMs or GenAI (prompting, retrieval-augmented generation, LLM APIs) to analytics or operations use cases.
• Familiarity with deep learning (PyTorch or TensorFlow) for sequence or log data.
• Exposure to MLOps basics: model versioning, monitoring and drift detection.
• Experience with Elasticsearch ML features or AIOps platforms.

Key Responsibilities

• Analyse large telemetry and event datasets (KPIs, alarms, downtime, crash reports) to find trends, anomalies and root causes.
• Build and validate ML models for anomaly detection, KPI forecasting, crash clustering and root-cause classification on network telemetry.
• Use AI and LLM tools to speed up analysis, such as summarising logs and crash reports, natural-language querying and automated insight generation.
• Work with engineering to move validated models from notebooks into production pipelines and dashboards.
• Define KPIs, metrics and data models with product and engineering teams.
• Write efficient queries and aggregations on Elasticsearch, SQL and Python-based pipelines.
• Leverage AI tools throughout the SDLC — from design and coding to testing, documentation, and troubleshooting — to accelerate delivery while ensuring output is reviewed, validated, and production-ready.