DATA SCIENTIST - COMPUTER VISION

Seeking a skilled Data Scientist with deep expertise in computer vision and experience working with the YOLO (You Only Look Once) object detection model. The ideal candidate will have a strong background in developing and deploying computer vision solutions and be proficient in analyzing complex datasets to drive actionable insights. This role will focus on leveraging computer vision to optimize processes, automate visual inspection tasks, and support various AI-driven initiatives within the organization.

Job Summary:

Seeking a skilled Data Scientist with deep expertise in computer vision and experience working with the YOLO (You Only Look Once) object detection model. The ideal candidate will have a strong background in developing and deploying computer vision solutions and be proficient in analyzing complex datasets to drive actionable insights. This role will focus on leveraging computer vision to optimize processes, automate visual inspection tasks, and support various AI-driven initiatives within the organization.

Key Responsibilities:

1. Computer Vision Model Development & Deployment
   • Design, develop, and deploy computer vision models, with a focus on object detection and image segmentation.
   • Implement and fine-tune YOLO-based models to detect, classify, and localize objects within images and videos.
   • Continuously improve model accuracy, speed, and performance to meet specific project requirements.
2. Data Analysis & Model Training
   • Collect, preprocess, and label large datasets to support training and validation of computer vision models.
   • Analyze image and video data to extract meaningful insights and identify patterns that can drive business improvements.
   • Apply data augmentation, transfer learning, and hyperparameter tuning to optimize model performance on complex datasets.
3. Cross-Functional Collaboration
   • Work closely with software engineers, product managers, and domain experts to understand project needs and integrate computer vision capabilities into broader applications.
   • Collaborate with machine learning engineers to deploy computer vision models in production environments.
   • Present findings and model outputs to non-technical stakeholders, providing recommendations based on data-driven insights.
4. Research & Innovation
   • Stay up to date with advancements in computer vision, object detection, and deep learning techniques, applying innovative solutions to ongoing projects.
   • Experiment with and evaluate alternative computer vision models (e.g., Faster R-CNN, SSD, EfficientDet) and frameworks to assess their suitability for specific tasks.
   • Contribute to developing and implementing best practices for model training, evaluation, and maintenance.
5. Performance Monitoring & Optimization
   • Set up monitoring tools and evaluate model performance in real-time to ensure consistency and accuracy of predictions.
   • Troubleshoot issues related to model performance, data quality, and integration with other systems.
   • Implement continuous improvement processes to refine and optimize computer vision solutions over time.

Qualifications:

• Education: Degree in Computer Science, Data Science, Electrical Engineering, or a related field, with a focus on machine learning or computer vision.
• Experience:
  • 5+ years of experience in data science, with a minimum of 1 year specializing in computer vision.
  • Hands-on experience implementing and tuning YOLO-based models and familiarity with different YOLO versions (YOLOv3, YOLOv4, YOLOv5, or YOLOv8).
  • Proven experience in working with large-scale datasets for training, validation, and testing of computer vision models.
• Technical Skills:
  • Proficient in Python, with experience in data science and deep learning libraries such as TensorFlow, PyTorch, and OpenCV.
  • Experience with data preprocessing and labeling tools for computer vision applications.
  • Familiarity with deep learning frameworks and cloud platforms (e.g., AWS, Azure, or GCP) for model deployment.
  • Strong understanding of model optimization techniques, including quantization and pruning, to ensure efficient performance.

Preferred Qualifications:

• Experience deploying computer vision models on edge devices.
• Familiarity with other object detection architectures, such as Faster R-CNN, SSD, and EfficientDet.
• Proficiency in working with version control (e.g., Git) and project management tools (e.g., Jira).