Senior Data Science Manager

Job Description

Develop Computer Vision Algorithms:

• Design, train, and optimize deep learning models for tasks such as object detection, segmentation, image classification, and pose estimation.
• Implement and refine image processing techniques for feature extraction, noise reduction, and data augmentation.

Build Scalable Models:

• Develop scalable and efficient pipelines for real-time video and image data processing.
• Collaborate with engineering teams to integrate vision models into production systems.

Data Analysis and Management:

• Collect, preprocess, and analyze large-scale datasets, including labeled and unlabeled visual data.
• Leverage techniques like supervised, unsupervised, and self-supervised learning.

Research and Innovation:

• Stay updated on the latest advancements in computer vision, deep learning, and AI.
• Experiment with state-of-the-art architectures like Transformers, GANs, or Diffusion Models to enhance model performance.

Collaborate Across Teams:

• Work closely with cross-functional teams, including data engineers, software developers, and domain experts, to understand business problems and deliver solutions.
• Present findings and insights to stakeholders using clear visualizations and reports.

Deploy and Monitor Models:

• Deploy models on cloud platforms or edge devices for real-time applications.
• Monitor model performance and implement strategies for continuous improvement.

Job Requirement

Education:

• Bachelor’s or higher degree in Computer Science, Data Science, Artificial Intelligence, or a related field.

Experience:

• Proven experience (3-5+ years) in applying computer vision techniques in real-world projects.
• Hands-on experience with large datasets, image processing libraries, and deep learning frameworks.

Technical Skills:

• Strong programming skills in Python; proficiency in frameworks like TensorFlow, PyTorch, or OpenCV.
• Experience with image and video annotation tools.
• Familiarity with cloud platforms and edge deployment.
• Knowledge of MLOps tools and practices for model deployment and monitoring.
• Understanding of algorithm optimization techniques for performance and scalability.

Soft Skills:

• Strong problem-solving skills with the ability to translate business needs into technical solutions.
• Effective communication skills to present complex technical findings to non-technical stakeholders.
• Collaborative mindset and ability to work in interdisciplinary teams.

Preferred Qualifications

• Experience with 3D computer vision, LiDAR data, or point cloud processing.
• Familiarity with advanced architectures like YOLO, Mask R-CNN, Vision Transformers, or CLIP.
• Knowledge of generative AI models and their applications in computer vision.
• Experience with AR/VR applications or simulations.