Senior Data Analyst, Network Analytics & AI
Pune
- Pay
- Salary not listed in the saved posting
- Work setup
- Unconfirmed
- Employment
Full-time — employment source
Employment type Full Time
Read the full posting
What you’ll work on
Full postingAnalyse 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.
Work with engineering to move validated models from notebooks into production pipelines and dashboards.
From the employer’s posting
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.
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.
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.
What you’ll bring
All qualificationsCore experience
- 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.
- 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 experience
- Experience with the ELK stack (Elasticsearch, Logstash, Kibana) and Kafka.
- 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.
Qualification wording
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.
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.
Experience with the ELK stack (Elasticsearch, Logstash, Kibana) and Kafka.
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.
Tools in this posting
- Python
- SQL
- AWS
- Elasticsearch
- Kibana
- Kubernetes
- Logstash
- NumPy
- pandas
- PyTorch
- TensorFlow
- Xgboost
- Kafka
- scikit-learn
Source — Tool mentions in context
- 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.
- 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.
- 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.
Preferred Qualifications - Experience with the ELK stack (Elasticsearch, Logstash, Kibana) and Kafka. - Telecom domain knowledge (RAN, 4G/5G KPIs, O-RAN).
- Exposure to MLOps basics: model versioning, monitoring and drift detection. - Experience with Elasticsearch ML features or AIOps platforms. Key Responsibilities
- 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.
- 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).
Job description
We are hiring a Senior Data Analyst to turn 5G/LTE network telemetry, crash data and operational metrics into insights that improve network reliability and customer experience.
You' will own analytics from raw data to executive dashboards, and work closely with engineering, product and customer operations.
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.
- 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.
Preferred Qualifications
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.
Employment type
Full Time
Your next step
- Have your CV and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the salary range before committing time to the process.
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Source & posting history
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- Pay
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- Location & working pattern
Pune
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- Status in our records
- Active
- First seen by us
- Sep 28, 2026
- Recorded sightings
- 1
- Employer says posted
- Sep 24, 2026
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