Data Analyst

Role Overview

Join GE Vernova’s Power Conversion & Storage business as a Data Analyst to design, develop, and deliver AI-driven solutions that improve efficiency and decision-making across the business. Collaborate closely with domain experts and cross-functional teams to apply artificial intelligence, generative AI, and machine learning to real-world industrial challenges.

What You Will Do

Design, develop, and implement AI solutions, including generative AI, machine learning models, neural networks, and optimization algorithms. Select the appropriate modelling approach for each problem and articulate the trade-offs behind that choice. Own the machine learning lifecycle: dataset construction, metrics definition, acceptance criteria in accordance with the stakeholders needs, evaluation strategies.

Why It Might Be a Fit

This role requires a strong analytical and strategic thinking, with a high degree of self-motivation and a structured, goal-oriented working style. You will work closely with technical, operational, business and IT teams to ensure AI solutions meet business and industry requirements for safety, reliability, performance, and scalability.

Requirements

• Bachelor/Master’s degree in Engineering, Computer Science, Data Science, Applied Mathematics, or a related field
• Several years (2 to 4) of professional experience in artificial intelligence, machine learning, data science, software engineering, or a comparable technical role
• Strong programming skills in Python, C++, or similar languages, and proficiency with modern development environments such as VS Code
• Hands-on experience with machine learning and deep learning frameworks such as TensorFlow and/or PyTorch, as well as classical ML tooling (e.g. scikit-learn, gradient boosting methods)
• Experience with time-series analysis, optimization methods, and data-driven model development
• Practical experience with GenAI and their surrounding stacks (RAG, vector databases, A2A)
• Experience handling unstructured data; technical documents, specifications, reports; alongside structured and tabular data
• Solid grounding in cloud services and architecture (Azure, AWS)
• Working knowledge of data engineering fundamentals: SQL, data pipelines, and structured/unstructured data handling
• Familiarity with MLOps practices, model deployment, and integration into production environments is an advantage

Benefits

• Relocation assistance

Tagged as: Data Analysis