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Sr Analytics Engineer
GE Vernova
Bengaluru, IND
Full-time
Senior · 5+ years experience
Onsite
Discovered 3 days ago
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Role Overview
Roles and Responsibilities
• Partner with domain experts, engineering teams, and cross-functional stakeholders to identify high-impact analytics opportunities
• Develop, test, and deploy Python-based analytics solutions using operational, sensor, and event data from power plant assets
• Apply statistical methods, time-series analysis, machine learning techniques and AI to detect anomalies, identify failure patterns, and support diagnostics
• Perform data wrangling, exploratory data analysis, feature engineering, and root cause analysis on complex industrial datasets
• Design, build, and maintain scalable analytics pipelines for batch and near-real-time processing
• Develop dashboards, visualizations, and interactive analytics tools that provide insights for engineers to investigate equipment behavior, analyze trends, and troubleshoot customer issues and improve product design.
• Translate user needs into technical solutions and iterate based on stakeholder feedback to improve usability and business impact
• Validate analytics performance, monitor deployed solutions, and continuously improve model accuracy, robustness, and interpretability
• Follow software development best practices, including version control, testing, code review, and documentation
• Document data sources, methodologies, assumptions, and solution design to support maintainability, reproducibility, and knowledge sharing
• Stay current with advances in anomaly detection, time-series analytics, and applied machine learning and AI, and evaluate their practical use in industrial applications
Key Skills for This Role
PythonPandasNumPySciPyscikit-learnPyTorch
Full Job Posting
Roles and Responsibilities
- Partner with domain experts, engineering teams, and cross-functional stakeholders to identify high-impact analytics opportunities
- Develop, test, and deploy Python-based analytics solutions using operational, sensor, and event data from power plant assets
- Apply statistical methods, time-series analysis, machine learning techniques and AI to detect anomalies, identify failure patterns, and support diagnostics
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