GE Vernova Hiring: Data Analyst 2026:-
GE Vernova is hiring a Data Analyst for its Power Conversion & Storage business in Chennai, Tamil Nadu. This opportunity is suitable for professionals with 2–4 years of experience in Artificial Intelligence (AI), Machine Learning (ML), Data Science, Software Engineering, or related technical fields.
| Company: | GE Vernova |
| Job Role: | Data Analyst |
| Job Type: | Full-Time |
| Experience Required: | 2–4 years |
| Salary: | As per Company Standards |
| Application Deadline: | ASAP |
Role Overview:-
As a Data Analyst at GE Vernova, you will work with domain experts, software engineers, business teams, and IT professionals to develop practical AI and data-driven solutions for real-world industrial challenges.
The role requires strong technical judgment to determine whether a problem is best addressed through classical machine learning, deep learning, generative AI, optimization techniques, or conventional software development.
You will contribute throughout the solution lifecycle, from understanding business requirements and preparing data to developing models, integrating applications, evaluating performance, and supporting deployment.
Educational Requirements:-
Candidates should have one of the following qualifications:
- Bachelor’s degree in Engineering, Computer Science, Data Science, Applied Mathematics, or a related discipline.
- Master’s degree in Engineering, Computer Science, Data Science, Applied Mathematics, or a related discipline.
Key Skills Required:-
1. Technical Skills
- Programming: Python, C++, or similar programming languages.
- Machine Learning: Scikit-learn, gradient boosting methods, and classical ML algorithms.
- Deep Learning: TensorFlow and/or PyTorch.
- Generative AI: Large language models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, and A2A-related technologies.
- Data Analytics: Exploratory data analysis, statistical analysis, trend identification, and performance analysis.
- Time-Series Analysis: Forecasting, predictive modelling, and operational data analysis.
- Data Engineering: SQL, data pipelines, data processing, and structured/unstructured data management.
- Cloud Platforms: Microsoft Azure and AWS.
- Development Tools: Visual Studio Code (VS Code) and modern software development environments.
- MLOps: Model deployment, production integration, monitoring, and lifecycle management.
- Optimization: Optimization algorithms and data-driven model development.
- Data Types: Tabular datasets, technical documents, specifications, reports, and other unstructured information.
2. Business and Functional Skills
- Translating business requirements into technical solutions.
- Identifying AI and analytics use cases that deliver measurable business value.
- Assessing data quality, availability, access rights, and confidentiality.
- Understanding industrial processes, electrification, power systems, or related technical domains.
- Evaluating AI tools, cloud platforms, and software vendors.
- Preparing technical specifications and acceptance criteria.
- Supporting AI governance, cybersecurity, and data protection requirements.
- Understanding predictive maintenance, anomaly detection, forecasting, and quality prediction.
3. Soft Skills
- Strong analytical and problem-solving abilities.
- Strategic thinking and sound technical judgment.
- Excellent written and verbal communication in English.
- Ability to explain technical concepts to non-technical stakeholders.
- Cross-functional collaboration and teamwork.
- Structured, goal-oriented, and self-motivated working style.
- Strong documentation and attention to detail.
- Adaptability and willingness to learn emerging technologies.
Roles and Responsibilities:-
1. AI and Machine Learning Solution Development
- Design, develop, and implement AI and machine learning solutions for industrial and business applications.
- Build generative AI applications, neural networks, optimization algorithms, and predictive models.
- Select appropriate modelling approaches based on business requirements, available data, complexity, and expected outcomes.
- Define datasets, model evaluation metrics, acceptance criteria, and testing strategies.
- Convert operational challenges into scalable AI-enabled applications and workflows.
- Support model deployment and integration into existing software systems and enterprise platforms.
- Monitor deployed models for performance degradation, data drift, and operating costs.
2. Technical Implementation and Solution Architecture
- Collaborate with domain experts to identify suitable AI use cases and technical requirements.
- Design and document end-to-end solution architectures, including data sources, processing pipelines, models, applications, and enterprise integrations.
- Develop cloud-ready and on-premises solutions aligned with IT, cybersecurity, and data-governance requirements.
- Integrate AI capabilities into hardware, software, and business process environments.
- Help develop reliable, maintainable, scalable, and production-ready solutions.
- Ensure technical implementations meet industrial requirements for performance and reliability.
3. Data Strategy and Advanced Analytics
- Collect, process, structure, and analyze large-scale operational and business datasets.
- Assess data readiness, including data availability, quality, labelling requirements, access permissions, and confidentiality.
- Identify patterns, trends, anomalies, and opportunities for operational improvement.
- Develop analytics and AI solutions for predictive maintenance, forecasting, anomaly detection, and quality prediction.
- Support cost analysis, performance prediction, technical document analysis, and knowledge-support applications.
- Work with structured and unstructured data to generate actionable insights.
4. AI Solution Delivery and Partner Management
- Prepare technical specifications, statements of work, and acceptance criteria for internal teams and external contractors.
- Evaluate AI platforms, software tools, and vendors through technical assessments, benchmarking, and proof-of-concept testing.
- Review technical designs, source code, models, and implementation quality.
- Coordinate technical follow-ups, acceptance testing, and delivery reviews.
- Maintain technical ownership of delivered solutions and ensure they meet agreed requirements.
- Prepare documentation and support knowledge transfer to ensure solutions remain maintainable after handover.
5. Cross-Functional Collaboration
- Work closely with business, engineering, operations, IT, and domain teams.
- Translate complex technical requirements into practical AI and analytics solutions.
- Communicate findings, limitations, risks, and expected benefits to relevant stakeholders.
- Align AI initiatives with business priorities, operational goals, and technical constraints.
- Support end-user training, onboarding, and feedback collection.
- Measure the business and operational benefits achieved after deployment.
6. Innovation and Continuous Improvement
- Evaluate emerging technologies such as edge AI, synthetic data, reinforcement learning, and large language models.
- Track developments in AI, machine learning, and digital engineering tools.
- Identify practical opportunities to adopt new technologies based on measurable benefits rather than hype.
- Conduct experiments and proof-of-concept projects to validate potential solutions.
- Share knowledge and contribute to continuous improvement initiatives.
- Explore innovation management and patent-related opportunities where relevant.
About GE Vernova:-
GE Vernova is an energy-focused company working to advance electrification and decarbonization. Its Power Conversion & Storage business supports industrial and energy applications through power conversion technologies, storage solutions, and related engineering capabilities.
The company uses digital technologies, data analytics, and AI-driven innovation to improve the efficiency, reliability, and performance of industrial operations.
GE Vernova Hiring: Data Analyst 2026 Application Process:-
Apply Link:- Click Here To Apply
Note:- Only the shortlisted candidates will be notified for the further interview process.
