Data Scientist

About This Role

Developing and deploying AI-driven Machine Health Models for predictive maintenance, equipment health monitoring, and production quality improvement is a key focus of this Data Scientist role. Working alongside senior data scientists, you will analyze manufacturing datasets, build supervised and unsupervised AI models, deploy production-ready solutions, and help customers optimize their manufacturing operations.

Responsibilities

• Analyze large-scale manufacturing and semiconductor datasets, including time-series sensor data
• Perform data cleansing, feature engineering, and exploratory data analysis
• Generate actionable insights to improve equipment reliability, product quality, and manufacturing efficiency
• Develop supervised and unsupervised machine learning models for equipment health prediction and fault detection
• Build predictive models to detect anomalies, reduce quality excursions, and improve manufacturing performance
• Evaluate and optimize models using appropriate statistical and machine learning techniques
• Design, develop, and maintain MLOps pipelines for model deployment and lifecycle management
• Support deployment of AI models into customer production environments
• Monitor model performance and continuously improve accuracy and reliability
• Work with customers, engineers, and cross-functional teams to understand manufacturing challenges and propose AI solutions

Requirements

• Degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related discipline
• 1–3 years of experience in data analytics, machine learning, or AI development; fresh graduates with relevant project experience are welcome to apply
• Proficiency in English and Mandarin (written and spoken), as the role involves regular communication with Mandarin-speaking clients and stakeholders
• Proficiency in Python
• Solid understanding of statistical analysis and ML/DL frameworks such as Scikit-learn, LightGBM, XGBoost, TensorFlow, and PyTorch
• Experience with data visualization libraries such as Plotly and Seaborn
• Good software engineering practices: modular code, version control, and testing
• Strong sense of responsibility and ownership of deliverables
• Excellent organizational, analytical, and problem-solving skills
• Able to work independently and collaborate effectively in cross-functional teams

Benefits

• Apply AI to real production challenges at leading semiconductor and advanced manufacturing companies
• Structured training and mentorship from experienced data scientists
• Hands-on exposure to the full AI lifecycle, from data to deployment