Senior Data Analyst, Network Analytics – AI

Parallel Wireless

. Analyse large telemetry and event datasets, including KPIs, alarms, downtime and crash reports, to find trends, anomalies and root causes.

Posted 9/24/2026full-timePune • IndiaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in data analytics, machine learning model development, and anomaly detection within high-volume datasets. Proficient in SQL and Python, with a strong understanding of statistics and time-series analysis, particularly in the telecom domain.

Highest-signal resume keywords

Data AnalyticsMachine Learning Model DevelopmentSQL ProficiencyPython (Pandas, NumPy)Telecom Domain Knowledge

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data AnalysisAnomaly DetectionTime-Series AnalysisFeature EngineeringModel EvaluationStatistical AnalysisML Libraries (Scikit-Learn, XGBoost)Time-Series Forecasting (Prophet, ARIMA)Unsupervised Anomaly DetectionDeep Learning (PyTorch, TensorFlow)

Tools & Technologies

ElasticsearchELK StackKubernetesAWSKafkaMLOpsAIOps Platforms

Industry Keywords

TelecomRAN4G/5G KPIsO-RANData Engineering Workflows

Tech Stack

Tools & technologies

AWSElasticSearchKafkaKubernetesLogstashNumpyPandasPythonPyTorchScikit-LearnSDLCSQLTensorflow

About the role

Key responsibilities & impact

Analyse large telemetry and event datasets, including KPIs, alarms, downtime and 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 for log and crash-report summarisation, 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 for design, coding, testing, documentation and troubleshooting, ensuring outputs are reviewed, validated and production-ready.
Own analytics from raw data through executive dashboards and collaborate with engineering, product and customer operations.

Requirements

What you’ll need

Bachelor's or Master's in Computer Science, Statistics, Engineering or a related field.
10+ years of experience, including 5+ years in data analytics and at least 2 years 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, data leakage and class imbalance.
Experience with the ELK stack (Elasticsearch, Logstash, Kibana) and Kafka.
Telecom domain knowledge (RAN, 4G/5G KPIs, O-RAN).
Familiarity with Kubernetes, AWS or data engineering workflows.
Experience applying LLMs or GenAI 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.