Global Quality Intern, Data Science & Anomaly Detection

About our group:

The Global Quality AI/Process Control (AI/PC) team is focused on leveraging data science and machine learning to detect anomalies, improve predictive insights, and link upstream CTQs/KPIVs to reliability outcomes. We develop reproducible AI pipelines and scalable models that contribute directly to manufacturing and product reliability improvements.

About the role - you will:

• • Develop and test anomaly detection methods (z-score, IQR, clustering, ML-based outlier detection, time-series anomaly methods) for ORT/COH datasets.

  • Assist in benchmarking anomaly detection approaches against engineering rules (e.g., red/yellow dot sweeps, column swipes).

  • Build reproducible Python/KNIME pipelines for anomaly scoring, labeling, and classification at both head- and drive-level.

  • Partner with reliability engineers to link anomalies to upstream CTQs/KPIVs and propose early warning indicators.

  • Contribute to building MLOps-ready anomaly models that can scale across datasets (190k rows x 300+ columns per week).

  What Will You Learn and Embark on at the Start?

  You will start by learning Seagate’s reliability and quality datasets, focusing on anomaly detection problems. Your early tasks will include prototyping statistical and machine learning-based anomaly detection methods, and working with SMEs to validate model outputs.

  What You Would Ultimately Be Able to Be Proficient In?

  You will develop advanced skills in anomaly detection, machine learning, and scalable data science workflows. You will gain hands-on experience with big data pipelines and MLOps practices while contributing to systematic AI deployment.

About you:

• Analytical, with strong problem-solving skills and curiosity to explore patterns in data.
• Adaptable and eager to learn advanced AI/ML methods.
• Collaborative team player with good communication skills.
• Able to work independently with large datasets.

Your experience includes:

• Currently pursuing a degree in Data Science, Statistics, Computer Engineering, or Applied Mathematics.
• Strong background in statistics and machine learning (regression, clustering, classification, anomaly detection).
• Proficiency in Python (pandas, scikit-learn, PyOD, statsmodels) or KNIME workflows.
• Understanding of time-series data analysis and anomaly scoring methods.

Location:

The Shugart site (named after Seagate’s founder, Al Shugart) is a research and design center. Easily accessible from the One-North MRT Station, many employees choose to take mass transportation to work. Being a purpose-built building, The Shugart has excellent employee recreational facilities. Take an active break at our badminton courts, table tennis tables, in-house gym, and recreation rooms. Our yoga and Zumba classes are very popular. We also offer classes and interest groups in photography, gardening, and foreign languages, and have various on-site celebrations, and community volunteer opportunities

Location: Shugart, Singapore
Travel: None