AI HUB Data Scientist

Mission:

Design, validate and industrialize Machine Learning, Generative AI and production AI solutions for the Quality perimeter, transforming operational data into predictive insights, automated recommendations and measurable business outcomes.

• Main focus: ML modeling, GenAI solutions, model validation, production AI.

• Typical outputs: Predictive models, GenAI prototypes, model evaluation reports, production-ready AI components.

• Key interfaces: Quality business teams, data engineers, platform specialists, IT and data governance teams.

Key responsibilities:

• Translate Quality business problems into analytical objectives, model targets, success metrics, and data requirements.

• Develop ML, statistical, and GenAI models for prediction, classification, anomaly detection, root-cause support, and decision automation.

• Build prototypes using structured experimentation, feature engineering, model validation, and performance monitoring practices.

• Prepare models for production with robust documentation, lifecycle requirements, monitoring logic, and handover conditions.

• Monitor model performance, drift, and operational reliability, then recommend corrective actions when performance degrades.

Primary contribution:

Creates production-ready AI capabilities that transform Quality data into predictive insights, automated recommendations, and scalable decision support.

Professional experience and technical skills:

• Advanced Python practice, applied statistics, ML libraries, data science workflows, and experimentation methods.

• Practical knowledge of GenAI, prompt engineering, retrieval-augmented generation, model evaluation, and responsible AI practices.

• Experience working with structured and unstructured Quality, manufacturing, aftersales, customer voice, and operational data.

• Ability to move models from proof of concept to MVP and production, including monitoring, retraining, and lifecycle management.

• Clear communication of analytical choices, model trade-offs, limitations, and expected business impact.