Data Scientist

Responsibilities

• Help define engineering problems in the solar PV manufacturing process and contribute to analytical solution development in collaboration with cross-functional teams
• Interface with engineers and technicians to gather and translate requirements into actionable analytical support for process operations
• Support investigations into the root causes of process or equipment failures, unexpected shutdowns, and declines in yield, productivity, or efficiency
• Develop data-driven models using historical data to forecast outcomes, predict equipment health, and simulate operating conditions
• Operate, monitor, and maintain existing data-driven models, fault detection systems, and process control systems

Required Qualifications

• Master's degree in a quantitative discipline (e.g., Computer Science, Statistics, Industrial Engineering, Electrical Engineering, Chemical Engineering) with 2+ years of relevant experience (or a Bachelor's degree with 5+ years of relevant experience)
• Programming and data analysis skills using Python and SQL, with experience in relational databases (e.g., Oracle, MSSQL) and querying complex datasets
• Working knowledge of statistics and machine learning algorithms
• Experience using statistical and machine learning libraries (e.g., NumPy, SciPy, Scikit-learn); exposure to deep learning frameworks (e.g., PyTorch, TensorFlow, Keras) is a plus
• Strong analytical thinking and communication skills, with the ability to collaborate effectively across cross-functional teams

Preferred Qualifications

• Experience in a data-focused role within the solar PV, display device, or semiconductor manufacturing industry
• Experience with Fault Detection and Classification (FDC) and Advanced Process Control (APC) systems in a manufacturing environment
• Familiarity with cloud platforms (AWS, Azure, or GCP) for data pipelines, storage, or model deployment
• Exposure to retrieval-augmented generation (RAG) or LLM-based applications for industrial data and knowledge management