Descripción
You will be responsible for performing advanced data analysis and applying ML/AI (Machine Learning / Artificial Intelligence) techniques to improve yield, quality, and process understanding. In addition, you will build automated tools and models for prediction, monitoring, and engineering decision support. Primary Duties & Responsibilities Analyze large engineering datasets to identify trends and issues. Apply statistical and ML/AI methods for prediction and detection. Develop automated tools for analysis and reporting. Support root cause analysis using data-driven methods. Build and maintain models for yield and failure risk. Standardize data structures, KPIs, and analysis methods. Ensure data quality and consistency across systems. Collaborate with engineers to translate data into actions. Identify improvement opportunities proactively from data. Bridge Product Engineering with IT and data systems. Lead and Manage Identify improvement opportunities proactively from data. Collaborate with Product Engineering, WFAB, Backend, Process Integration, and Software teams. Work independently on analysis tasks; follow guidance from senior engineers and management to align priorities and escalate complex issues. Reports to Senior Manager, Product Operations. Education & Experience Master’s degree in Machine Learning, Mechatronics, Electronics, Nanotechnology, or a related field, including a thesis focused on industrial applications and AI/ML. Skills Strong analytical and problem-solving mindset with the ability to solve engineering challenges using data and AI/ML methods; proactive and results-oriented. Hands-on experience in data analysis, programming, and applying statistical or machine learning methods to real engineering problems. Minimum of 3 years of industrial experience or a proven track record of delivering similar projects. Expertise in ML/AI methods and working with large datasets. Experience with statistical tools such as JMP and Minitab. Programming skills in Pyt