Job at a glance
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.