Sr Analytics Engineer

GE Vernova

Bengaluru, India

Full Time

Posted September 11, 2026

Power Generation

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Listing evidence and provenance

Source: workday View original listing
Freshness: Open in the current public read path; this is not an employer guarantee.Last observed October 1, 2026
Classification: Modern power-system classification not yet published for this listing.Legacy source industry field: Power Generation
Pay provenance: Pay not available

Job Description

As a Senior Analytics Engineer on the Fleet Services Analytics team within Gas Power Engineering, you will develop analytics solutions and dashboard applications that help engineering teams identify equipment anomalies, diagnose component issues, and improve the reliability and performance of combined cycle power plant

Key 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

Requirements

Bachelor’s or Master’s degree in Engineering, Computer Science, Statistics, Mathematics, Data Science, or a related technical field
5-8 years of experience in analytics, data science, software development, or a related technical role.
Strong programming skills in Python , with hands-on experience using libraries such as Pandas, NumPy, SciPy, scikit-learn, Pytorch, Tensorflow and related tools.
Strong foundation in statistics, probability, data analysis, and time-series methods
Experience working with large, complex, and noisy real-world datasets
Experience developing production-quality analytics workflows, MLOps and CI-CD pipelines, dashboards or analytics applications
Strong problem-solving skills with the ability to translate engineering or business challenges into practical analytical solutions
Strong written and verbal communication skills, including the ability to explain technical concepts and analytical results to a range of stakeholders
Experience with anomaly detection, fault detection, diagnostics, predictive maintenance, or reliability analytics
Experience working with industrial, operational, IoT, or sensor data
Experience using AI tools like Github Copilot or Claude Code.
Experience developing dashboards or analytics applications using tools such as Plotly Dash, Streamlit, Tableau, or Power BI
Familiarity with cloud platforms, CI/CD pipelines, data engineering workflows, or MLOps practices
Familiarity with combined cycle power plants, rotating equipment, thermal systems, or related industrial domains
Experience supporting engineering, operations, or reliability teams with data-driven tools
Exposure to advanced time-series modelling or deep learning methods is a plus where relevant to practical use cases