Job Description Summary

GE Vernova’s Power Conversion & Storage business is at the forefront of the energy transition. We are seeking several highly skilled Data Analyst to join our teams to design, develop, and deliver AI-driven solutions that improve efficiency and decision-making across our business.
In this role, you will collaborate closely with domain experts and cross-functional teams to apply artificial intelligence, generative AI, and machine learning to real-world industrial challenges, helping accelerate innovation, productivity, and operational excellence.
A defining part of the role is technical judgment; choosing the right tool for each problem: classical machine learning, deep learning, GenAI, or pure software development when needed.

Job Description

• Key ResponsibilitiesAI & ML Solution Development & Integration
  • Design, develop, and implement AI solutions, including generative AI, machine learning models, neural networks, and optimization algorithms, to improve business process efficiency and effectiveness.
  • 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.
  • Translate business and operational needs into scalable AI-enabled tools, applications, and workflows.
  • Support the deployment and integration of AI/ML models into existing business and technical systems, software environments, and products where applicable, including monitoring for drift, performance degradation, and running cost.
•  Technical Implementation & Architecture
  • Collaborate with domain experts to identify high-value use cases and define technical requirements for AI solutions.
  • Define and document the solution architecture end to end: from data sources to the integration with the existing enterprise and technical IT landscape.
  • Design cloud-ready and on-premises solutions aligned with company IT, cybersecurity and data-governance standards.
  • Integrate AI/ML capabilities into hardware, software, and business process ecosystems in a way that supports reliability, usability, and maintainability.
  • Contribute to the development of robust, production-ready AI solutions suitable for industrial environments.
•  Solution Delivery, Partner & Contractor Management
  • Write clear technical specifications, statements of work, and acceptance criteria for work delivered by external contractors, software vendors, or internal digital teams.
  • Contribute to supplier, platform, and tool selection through structured technical evaluation, benchmarking, and proof-of-concept comparison.
  • Steer and review the work of internal & external partners: technical follow-up, design reviews, code and model reviews, quality gates, and acceptance testing; remaining the technical owner and guardian of the delivered solution.
  • Ensure solutions remain maintainable after handover through documentation, knowledge transfer, and clearly assigned ownership, so that delivered tools do not become orphaned.
•  Data Strategy & Analytics
  • Lead or support the collection, processing, structuring, and analysis of large-scale operational and business data.
  • Assess data readiness ahead of any development (availability, quality, labelling needs, access rights, confidentiality) and define strategies to close the gaps.
  • Identify patterns, trends, and performance improvement opportunities using advanced analytics and AI methods.
  • Develop data-driven solutions such as predictive maintenance, anomaly detection, quality and performance prediction, forecasting, cost analysis,  document and requirement analysis, and knowledge support tools.
•  Cross-Functional Collaboration
  • Work closely with technical, operational, business and IT teams to ensure AI solutions meet business and industry requirements for safety, reliability, performance, and scalability.
  • Communicate technical concepts clearly to both technical and non-technical stakeholders.
  • Help align AI initiatives with business priorities, operational goals, and constraints.
  • Support end-user adoption: training, onboarding, feedback loops and measurement of the benefits realized once the solution is live.
•  Continuous Innovation
  • Evaluate emerging technologies such as edge AI, synthetic data, reinforcement learning, and large language models for industrial and business applicability.
  • Stay current with developments in AI, machine learning, and digital tools, and recommend practical adoption opportunities.
  • Maintain an active technology watch on the AI tooling landscape and filter it, distinguishing capability gains from hype before proposing adoption.
  • Contribute to building an innovation-oriented culture through knowledge sharing, experimentation, and continuous improvement.
• 
  Education
  • Bachelor/Master’s degree in Engineering, Computer Science, Data Science, Applied Mathematics, or a related field.
•  Experience
  • Several years (2 to 4) of professional experience in artificial intelligence, machine learning, data science, software engineering, or a comparable technical role.
  • Experience developing and deploying AI/ML solutions in industrial, technical, or other complex operational environments is preferred.
•  Technical Expertise
  • 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.
•  Domain Knowledge (Secondary)
  • Sound knowledge of artificial intelligence, combined with a strong interest in emerging technologies and digital trends.
  • Understanding of industrial processes, electrification, power systems, or related technical domains, or business processes, is an advantage.
  • Awareness of the regulatory and governance context around AI (e.g. EU AI Act, GDPR) is a plus.
  • Experience in innovation management and/or patent-related work is a plus.
•  Personal Attributes
  • Proven ability to translate complex business and technical challenges into practical, scalable AI-driven solutions.
  • Strong analytical and strategic thinking, with a high degree of self-motivation and a structured, goal-oriented working style.
  • Strong documentation skills and attention to detail.
  • Collaborative mindset with the ability to work effectively across functions and disciplines.
  • Excellent written and verbal communication skills in English.

Additional Information