Sr Business Intelligence Analyst (Sr Data Scientist)

As a candidate for the position, you will be responsible for leading projects and collaborating with Automotive EMIA Operations plants, internal teams (IT, TEIS, Corporate Technology, CoE, SC), and external partners in the Smart Factory domain aligned with Operations Automotive EMIA Strategy. Your key responsibilities will include: - Leading Smart Factory improvement projects of existing applications in collaboration with internal and external stakeholders until the Ready To Deploy (RTD) level. - Utilizing your combined knowledge of computer science, applications, modelling, statistics, analytics, and maths to solve complex problems. - Identifying and interpreting data sources, managing large datasets, ensuring data consistency, merging data sources, and creating visualizations to facilitate data understanding. - Developing, validating, deploying, and providing training and coaching for Digital Factory Applications and ML models until the RTD level, contributing to waste elimination and efficiency improvements across all Operations functions. To excel in this role, you should possess the following qualifications: - Good understanding of a technology-driven manufacturing company and knowledge of core manufacturing technologies like Stamping, Plating, Assembly, and Molding. - Experience with data visualization tools such as PowerBi, Tableau, and ThingWorx. - Strong applied statistics skills including distributions, statistical testing, regression, etc. - Proficiency in programming languages like R, Python, and Java scripting. - Excellent pattern recognition, predictive modeling, and machine learning skills. - Familiarity with technologies like Jupyter Notebooks, Numpy, Pandas, Seaborn, Scikit-learn, PyTorch, TensorFlow, and exposure to recent developments in AI domains. - Database skills in on-premises and cloud-based environments, with AWS experience being advantageous. - Excellent presentation and communication skills in English. - Ability to simplify complex data science topics for broader understanding and acceptance. - Bachelor's or master's degree in data science. - Team player with a solution-focused problem-solving approach and a passion for technology, data, analytics, and statistics. Additionally, you should have working knowledge of: - Connectivity to databases using packages like sqlite3, pyodbc, postgresSQL, AWS Redshift. - Image classification algorithms like CNN, SVM, k-Nearest neighbor, MLPs, SGD. - Data preprocessing techniques, data normalization, and working with Big Data. - Experience in AWS and/or MS Azure for model building and deployment. - End-to-end implementation of ML models and real-time computer vision algorithms. - Exposure to vision systems, deep learning techniques, AutoML tools, and transfer learning for model accuracy. Make sure to demonstrate your expertise in these areas during the application process. As a candidate for the position, you will be responsible for leading projects and collaborating with Automotive EMIA Operations plants, internal teams (IT, TEIS, Corporate Technology, CoE, SC), and external partners in the Smart Factory domain aligned with Operations Automotive EMIA Strategy. Your key responsibilities will include: - Leading Smart Factory improvement projects of existing applications in collaboration with internal and external stakeholders until the Ready To Deploy (RTD) level. - Utilizing your combined knowledge of computer science, applications, modelling, statistics, analytics, and maths to solve complex problems. - Identifying and interpreting data sources, managing large datasets, ensuring data consistency, merging data sources, and creating visualizations to facilitate data understanding. - Developing, validating, deploying, and providing training and coaching for Digital Factory Applications and ML models until the RTD level, contributing to waste elimination and efficiency improvements across all Operations functions. To excel in this role, you should possess the following qualifications: - Good understanding of a technology-driven manufacturing company and knowledge of core manufacturing technologies like Stamping, Plating, Assembly, and Molding. - Experience with data visualization tools such as PowerBi, Tableau, and ThingWorx. - Strong applied statistics skills including distributions, statistical testing, regression, etc. - Proficiency in programming languages like R, Python, and Java scripting. - Excellent pattern recognition, predictive modeling, and machine learning skills. - Familiarity with technologies like Jupyter Notebooks, Numpy, Pandas, Seaborn, Scikit-learn, PyTorch, TensorFlow, and exposure to recent developments in AI domains. - Database skills in on-premises and cloud-based environments, with AWS experience being advantageous. - Excellent presentation and communication skills in English. - Ability to simplify complex data science topics for broader understanding and acceptance. - Bachelor's or master's degree in data science