Data Scientist at Micron Technology

, , Singapore -

Full Time

Start Date

Immediate

Expiry Date

19 Mar, 26

Salary

0.0

Posted On

19 Dec, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Computer Vision, Deep Learning, AI, Multimodal Analysis, Programming, Data Engineering, MLOps, Deployment, Visualization, Communication, Project Management, Technical Writing, Training, Mentoring, SQL, Python

Industry

Semiconductor Manufacturing

Description

Design and implement computer vision systems for object detection, image segmentation, anomaly detection, and automated inspection, including experience with RGB-D cameras and vision inspection systems for volume/density measurement and component verification. Develop and deploy deep learning models (CNNs, LSTM, transformer-based architectures) for tasks such as image classification, time-series anomaly detection, and multimodal sentiment analysis, leveraging frameworks like TensorFlow, PyTorch, and Keras. Apply advanced AI techniques including few-shot learning, generative AI, and multimodal fusion (text, image, time-series) to solve manufacturing and environmental monitoring challenges. Extract, cleanse, and analyze large-scale datasets from SQL databases, cloud platforms (AWS, GCP), and sensor networks, applying rigorous outlier detection and missing data handling. Lead cross-functional collaboration with engineering, operations, and quality teams, including experience in project management, technical writing, and training/mentoring engineers in data science tools (Python, Spotfire, Power Automate). Coordinate production deployment activities using MLOps best practices (MLflow, Apache Airflow), including model monitoring, data drift detection, versioning, and automated testing. Contribute to research and innovation through published patents, peer-reviewed papers, or presentations at top-tier conferences (e.g., IJCNN, CVPR, NeurIPS), and demonstrate ability to translate research into practical solutions. Communicate technical concepts and project outcomes effectively to both technical and non-technical stakeholders. Ph.D. or Master's degree in Computer Science, Data Science or AI. Minimum 2 years of hands-on experience developing and deploying scalable AI applications in manufacturing, semiconductor, or electronics industries. Computer Vision: At least 2 years of working experience in designing and deploying computer vision models for industrial applications, including object detection, image segmentation, and automated inspection using OpenCV, YOLO, and RGB-D cameras. Deep Learning & AI: At least 2 years of working experience with deep learning frameworks (TensorFlow, PyTorch, Keras), including CNNs, LSTM, transformer models, and generative AI. Multimodal Analysis: Proven ability to work with multimodal datasets (text, image, time-series), including sentiment analysis and fusion of multiple data types. Programming & Data Engineering: Strong Python programming skills; at least 2-years' working experience with SQL, Java, C++, and cloud platforms (AWS, GCP); familiarity with distributed computing frameworks (Spark, Hadoop). MLOps & Deployment: Experience with MLflow, Apache Airflow, Docker, and Git for robust model deployment and monitoring in production environments. Visualization & Communication: At least 2 years of working experience in visualization tools (Dash, Plotly, Spotfire, PowerBI) and technical writing for documentation and training. In working or research environment consistently demonstrated analytical and problem-solving skills, with a data-driven and research-oriented mindset for at least 2 years. Effective communicator and collaborator, with experience in cross-functional project management and training. Proven ability with at least 1-year working experience to work independently, manage multiple priorities, and deliver high-quality results in fast-paced, dynamic environments. proven track record of commitment to quality, continuous improvement, and adaptability in manufacturing settings. Prior experience in the semiconductor industry (e.g., Intel, STMicroelectronics) or electronics manufacturing. Hands-on involvement in integrating computer vision or NLP solutions into production systems. Contributions to patents, peer-reviewed publications, or top-tier conferences (IJCNN, CVPR, NeurIPS). Experience with large language models (LLMs), multimodal sentiment analysis, or generative AI. Professional training or teaching experience in data science or AI topics.

Responsibilities

Design and implement computer vision systems and develop deep learning models for various applications. Lead cross-functional collaboration and coordinate production deployment activities using MLOps best practices.