Junior Data Scientist – Qualité, Ingénierie, IA/ML

Role Overview

• 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.

Requirements

• 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.

Tech Stack

• ERP
• Python
• Scikit-Learn

Benefits

• Relocation Assistance Provided: Yes