Senior Data Scientist

Job Title

Location

Job Type

Experience

About the Role

We are seeking a Sr. Data Scientist with experience in Computer Vision to join our team. You will be responsible for building and maintaining models including but not limited to Vision Transformer neural networks. This role involves collaborating with data engineers and backend developers to deliver quality AI solutions.

Responsibilities

• Design and implement end-to-end machine learning workflows for image and computer vision applications, from data collection to model deployment.
• Collaborate with cross-functional teams, including data engineers, product managers, and domain experts, to define and prioritize machine learning initiatives.
• Document technical designs and model specifications, ensuring clarity and accessibility for stakeholders and team members.
• Ensure adherence to best practices in model development, deployment, and monitoring, in alignment with the overall AI strategy.
• Monitor model performance and implement strategies for continuous improvement and retraining as needed.
• Develop scalable and efficient deep learning models using PyTorch, optimizing for performance and resource utilization.

Qualifications

• Bachelors degree in Computer Science or a related field.
• 4-7 years of hands-on experience in developing and deploying machine learning models, particularly in computer vision tasks.
• Proficient in using PyTorch for developing deep learning models, with a strong understanding of CNNs, transfer learning, vision transformers, and data augmentation techniques.
• Solid understanding of computer vision concepts, including image classification, object detection, and image segmentation.
• Strong programming skills in Python, with experience in data manipulation libraries such as NumPy and Pandas.
• Experience with version control systems like Git.
• Excellent analytical and problem-solving skills, strong communication abilities, and a collaborative mindset.

Preferred Qualifications

• Experience with cloud platforms (e.g., AWS, GCP, Azure) and their ML services, particularly those related to model deployment and GPU training.
• Understanding of MLOps principles and practices, including model monitoring, versioning, and governance.
• Knowledge of GPU computing and tools for managing GPU resources (e.g., CUDA, cuDNN).