Junior Data Scientist – Qualité, Ingénierie, IA/ML
Budapest, Budapest, Hungary
Full Time
6 hours ago
No Sponsorship
Key skills
ERPPythonScikit-LearnAIMachine LearningMLLarge Language Modelsscikit-learnXGBoostSalesforceCRMSAPCollaboration
About this role
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.