Junior Data Scientist - Quality Engineering & AI/ML Implementation

Job Description Summary

GE Vernova is accelerating the path to more reliable, affordable, and sustainable energy, while helping our customers power economies and deliver the electricity that is vital to health, safety, security, and improved quality of life. Are you excited at the opportunity to electrify and decarbonize the world?
We are seeking a curious, analytically sharp and digitally passionate Junior Data Scientist to join our HQ Quality Engineering & AI/ML Implementation team - a team where collaboration and participative leadership are not just words, but the way we work every day.
This is your opportunity to create real impact from day one. As a core member of our quality engineering team, you will be at the forefront of our Quality 5.0 vision - where data intelligence and AI-powered tools redefine how we manage, predict and prevent quality issues across GE Vernova's global business. You will analyse quality data from across our enterprise systems, define how data assets from platforms like SAP and Salesforce can be leveraged, and translate those insights into Machine Learning models and LLM-powered solutions that make quality proactive rather than reactive.

• Analyzing quality data from multiple enterprise systems (including SAP, Salesforce and others) to identify patterns, gaps and opportunities for data-driven quality improvements.
• Defining which data assets are relevant for quality use cases and specify how data from different systems should be accessed, interpreted and used - in close collaboration with our Digital execution team.
• Translating business quality challenges into concrete data science and AI/ML problem statements, acting as the domain-aware bridge between Quality Engineering and the Digital team.
• Supporting our Digital team in delivering centralized quality reporting solutions, providing harmonized insights and KPIs to business stakeholders across GE Vernova's global business lines.
• Developing and validating Machine Learning models that support preventive and predictive quality use cases, contributing to our Quality 5.0 vision.
• Leveraging Large Language Models (LLMs) and prompt engineering to build intelligent quality tools that augment human decision-making and automate quality workflows.
• Collaborating closely with Data Engineers and AI/ML engineers to ensure that data requirements are correctly understood and implemented at pipeline and infrastructure level.
• Building and maintaining a deep understanding of Semantic Data Models to ensure consistent data interpretation across quality applications and business systems.
• Engaging with internal business line stakeholders to understand their quality data needs, gather requirements and iterate solutions based on real-world feedback.
• Contributing to the evolution of the Digital Quality Suite, bringing innovative ideas and a forward-thinking mindset to continuously improve our quality tooling landscape.
• Documenting analytical findings, model performance and data definitions clearly to ensure transparency and reproducibility across the team.
• Stay current with the latest advancements in AI, ML and data science, proactively proposing new approaches that could enhance our quality solutions.

• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering or a related technical field.
• Proven hands-on experience in data science, data analysis or a related discipline - experience gained in a startup or fast-paced digital environment is a strong plus.
• Proficiency in Python for data analysis, statistical modelling and ML development.
• Foundational to intermediate experience with Machine Learning frameworks and methodologies (e.g., scikit-learn, XGBoost, or similar).
• Familiarity with Large Language Models (LLMs) and basic prompt engineering techniques and their practical application in business contexts.
• Understanding of Semantic Data Models and data modelling concepts across heterogeneous systems.
• Experience working with structured and unstructured data from enterprise systems (e.g., ERP, CRM platforms).
• Strong collaborative mindset - able to define requirements clearly and work effectively with a distributed Digital execution team.
• Proactive, intellectually curious and comfortable operating in a dynamic, evolving environment.
• Fluent in English (written and spoken); additional languages are a plus.

• Ability to analyse, interpret and communicate complex data findings to both technical and non-technical audiences.
• First experience with enterprise platforms such as SAP, Salesforce or similar ERP/CRM systems from a data consumption perspective.
• Experience with AWS cloud services from a data science perspective (e.g., SageMaker, S3, Lambda).
• Knowledge of SQL and experience querying relational or NoSQL databases.
• Exposure to MLOps principles - model versioning, experiment tracking, deployment basics.
• Understanding of data governance principles and responsible AI practices.
• First Experience in international, multicultural team environments and working across time zones.