Data Scientist II

2–4 years of hands-on experience in data science, machine learning, computer vision, or a related field.

Strong programming experience in Python, with experience using common data science and machine learning libraries.

Hands-on experience developing and training deep learning models using frameworks such as PyTorch, TensorFlow, or similar.

Understanding of fundamental machine learning concepts, including supervised learning, self-supervised learning, model evaluation, feature engineering and optimization.

Familiarity with computer vision concepts and techniques, such as instance and semantic segmentation, depth estimation, object detection, image classification, or related tasks.

Experience working with large datasets, including data preprocessing, dataset preparation, and exploratory data analysis.

Strong analytical and problem-solving skills with the ability to investigate complex model and data-related issues and develop appropriate solutions within a defined problem space .

Ability to design and execute experiments, interpret results, and clearly communicate findings and recommendations.

Familiarity with cloud platforms such as AWS and GPU-based model training environments.

Effective written and verbal communication skills and the ability to collaborate with cross-functional teams.

Familiarity with version control systems, such as Git, and collaborative software development practices.

Experience working with aerial, satellite, geospatial, or other large-scale imagery datasets.

Experience with modern deep learning architectures such as Vision Transformers, Foundation Models and CNNs.

Exposure to MLOps practices, including experiment tracking, model versioning, reproducible training pipelines, and model lifecycle management.