Senior Data Science Manager (Computer Vision)

Developing and deploying cutting-edge algorithms and machine learning models to analyze visual data and solve complex challenges. This role will involve applying deep learning, image processing, and computer vision techniques to contribute to the organization’s key projects in industries like manufacturing and agriculture (AI-driven solutions for Livestock Management).

Key Responsibilities

• 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.

- Bachelor’s or higher degree in Computer Science, Data Science,

Artificial Intelligence, or a related field.

- 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.

- 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.

- 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.