Data Scientist - Computer Vision & Deep Learning

Key Responsibilities:

Defect Detection & Inspection Systems:

- Design, develop, train, and optimize computer vision models for detecting defects in high-resolution silicon die images.

- Build robust automated inspection pipelines capable of identifying minute defects with high precision and recall.

- Improve defect classification accuracy through advanced deep learning architectures.

- Develop scalable solutions for industrial quality control and semiconductor inspection applications.

Image Processing & Enhancement:

- Implement advanced image processing techniques to improve image quality and analysis performance.

- Enhance microscopic image clarity for improved downstream analytics.

- Develop preprocessing pipelines for handling complex image artifacts.

Deep Learning Model Development:

- Build and deploy deep learning models using frameworks such as TensorFlow and PyTorch.

- Fine-tune pre-trained models to improve detection performance.

Multi-Modal Imaging Analytics:

- Process and analyze multi-modal imaging datasets.

- Integrate information from diverse imaging modalities for enhanced defect analysis.

- Develop AI models capable of learning from heterogeneous image sources.

- Improve image interpretation capabilities through multimodal learning techniques.

- Implement model compression techniques.

- Perform hyperparameter tuning and architecture optimization.

- Deploy models for real-time and near-real-time applications.

Research & Innovation:

Semiconductor Inspection Technologies:

- Evaluate emerging algorithms and incorporate innovative approaches into existing systems.

- Conduct experiments to benchmark and improve model performance.

Collaboration & Stakeholder Management:

- Partner with data scientists, engineers, domain experts, and product teams to define AI strategies.

- Translate business requirements into scalable machine learning solutions.

- Present findings, model performance metrics, and technical recommendations to stakeholders.

- Support knowledge sharing and mentor junior team members.

Required Skills & Qualifications:

Technical Skills:

- Strong proficiency in Python programming.

- Expertise in Computer Vision and Image Processing techniques.

- Hands-on experience with: OpenCV, TensorFlow, PyTorch, and Scikit-learn.

- Deep understanding of: CNNs, Object Detection, Segmentation Models, Transfer Learning, and Feature Engineering.

- Experience with image annotation and labeling tools.

- Familiarity with model deployment frameworks and MLOps practices.

- Knowledge of GPU optimization and parallel computing is desirable.

Machine Learning Expertise:

- Supervised and unsupervised learning techniques.

- Deep Learning model development and evaluation.

- Performance optimization and model explainability.

- Statistical analysis and experimental design.

Preferred Domain Experience:

- Semiconductor manufacturing, defect detection systems, industrial inspection, microscopic image analysis, automated visual inspection, and multi-modal imaging applications.

Educational Qualification:

- Master's degree (preferred) in Data Science, Computer Vision, Artificial Intelligence, or a related field.

Preferred Competencies:

- Strong analytical and problem-solving skills.

- Ability to work in fast-paced and research-driven environments.

- Excellent communication and stakeholder management capabilities.

- Passion for innovation and applied AI research.

- Experience working with large-scale image datasets.