Industrial Data Scientist - Vision & AI for Manufacturing

Micron Technology, Inc; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

Micron Technology, Inc. in Singapore seeks a Data Scientist to transform manufacturing data into actionable insights. You will build computer vision, deep learning, and multimodal models to improve inspection and AI-driven processes across production lines.

The role involves cleansing large datasets from SQL and cloud sources, deploying ML models with MLOps practices, and collaborating with engineers, operations, and quality teams to translate research into practical solutions.

Qualifications

• Bachelor's degree in Computer Science, Data Science or AI.
• Master's degree or PhD in Computer Science, Data Science or AI.
• 2-3 years hands-on experience deploying scalable AI apps in manufacturing/electronics.
• Strong Python programming and data engineering skills.

Responsibilities

• Design and implement computer vision systems for industrial applications.
• Develop and deploy deep learning models (CNNs, LSTM, transformers) using PyTorch/TensorFlow/Keras.
• Handle multimodal datasets (text, image, time-series) and sentiment analysis.
• Collaborate with cross-functional teams and mentor data science engineers.
• Coordinate deployment with MLOps tools (MLflow, Airflow) and ensure monitoring.

Skills

Python

SQL

OpenCV

YOLO

PyTorch

TensorFlow

Keras

MLflow

Apache Airflow

Docker

Spark

Plotly

Education

Bachelor's degree in Computer Science, Data Science or AI

Master's degree or PhD in Computer Science, Data Science or AI

Tools

OpenCV

YOLO

PyTorch

TensorFlow

Keras

MLflow

Apache Airflow

Docker

Git

Dash/Plotly

Spark

GCP/AWS

Job description

Micron Technology, Inc. in Singapore seeks a Data Scientist to transform manufacturing data into actionable insights. You will build computer vision, deep learning, and multimodal models to improve inspection and AI-driven processes across production lines.

The role involves cleansing large datasets from SQL and cloud sources, deploying ML models with MLOps practices, and collaborating with engineers, operations, and quality teams to translate research into practical solutions.