Lead projects and work together with our Automotive EMIA Operations plants, internal (IT, TEIS, Corporate Technology, CoE, SC, ..) and external partners in the area of Smart Factory (Applications, ML, AI, DL), aligned with Operations Automotive EMIA Strategy.
Lead Smart Factory improvement projects (Enhancements) of existing applications together with internal / external supplier and customers (project management) until the level of RTD (Ready To Deploy)
Use a combined knowledge of computer science and applications, modelling, statistics, analytics and maths to solve problems.
Identify and interpret data sources, manage large amounts of data, merge data sources together, ensure consistency of data-sets, create visualizations to aid in understanding data.
Develop, Validate and Deploy (including Training and Coaching of the users) until the level of RTD (Ready To Deploy), Digital Factory Applications, ML models, contributing to waste elimination and efficiency improvements within all Operations functions (Manufacturing, Quality, SC, LOG, ….), this together with our internal and external partners.
Desired Candidate
Good understanding of a technology-driven manufacturing company.
Knowledge of the core manufacturing technologies: Stamping, Plating, Assembly, Molding.
Experienced with data visualization tools like Power BI, Tableau, ThingWorx, etc.
Good applied statistics skills, such as distributions, statistical testing, regression, etc.
Development experience in programming languages R, Python, etc.
Knowledge and experience in Java scripting.
Excellent pattern recognition and predictive modeling skills.
Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, neural networks, and deep learning.
Experience with technologies like Jupyter Notebooks, NumPy, Pandas, Seaborn, Scikit-learn, PyTorch, and TensorFlow.
Exposure to recent developments in Deep Learning, Generative AI, and Agentic AI domains.
Database skills — both on-premises and cloud-based.
Experience in AWS is an advantage; functional knowledge of AWS platforms such as SageMaker, S3, and Redshift.
Excellent presentation skills in both spoken and written English.
Excellent and strong communicator.
Ability to translate, explain, and simplify complex data science topics for easy understanding, increasing general acceptance levels.
Bachelor’s or Master’s degree in Data Science.
Team player with a solution-focused, problem-solving mentality.
Change agent with high execution levels, including training and coaching talent
Good understanding of a technology-driven manufacturing company.
Knowledge of the core manufacturing technologies: Stamping, Plating, Assembly, Molding.
Experienced with data visualization tools like Power BI, Tableau, ThingWorx, etc.
Good applied statistics skills, such as distributions, statistical testing, regression, etc.
Development experience in programming languages R, Python, etc.
Knowledge and experience in Java scripting.
Excellent pattern recognition and predictive modeling skills.
Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, neural networks, and deep learning.
Experience with technologies like Jupyter Notebooks, NumPy, Pandas, Seaborn, Scikit-learn, PyTorch, and TensorFlow.
Exposure to recent developments in Deep Learning, Generative AI, and Agentic AI domains.
Database skills — both on-premises and cloud-based.
Experience in AWS is an advantage; functional knowledge of AWS platforms such as SageMaker, S3, and Redshift.
Excellent presentation skills in both spoken and written English.
Excellent and strong communicator.
Ability to translate, explain, and simplify complex data science topics for easy understanding, increasing general acceptance levels.
Bachelor’s or Master’s degree in Data Science.
Team player with a solution-focused, problem-solving mentality.
Change agent with high execution levels, including training and coaching talent