Data Scientist - Computer Vision & Generative AI

As a Data Scientist specializing in Computer Vision and Generative AI at a Renewable Energy/Solar Services company, your role will involve developing AI-driven solutions to revolutionize the analysis, monitoring, and optimization of solar infrastructure through image-based intelligence. Your work will enable intelligent automation, predictive analytics, and visual understanding in areas such as fault detection, panel degradation, site monitoring, and more. **Key Responsibilities:** - Design, develop, and deploy computer vision models for tasks like object detection, classification, segmentation, and anomaly detection. - Utilize generative AI techniques such as GANs and diffusion models to simulate environmental conditions, enhance datasets, and generate synthetic training data. - Build ML pipelines for end-to-end model training, validation, and deployment using Python and modern ML frameworks. - Analyze drone, satellite, and on-site images to extract insights for solar panel performance, wear-and-tear detection, and layout optimization. - Collaborate with cross-functional teams to understand business needs and develop scalable AI solutions. - Stay updated with the latest models, frameworks, and techniques to enhance model performance and robustness. - Optimize image pipelines for performance, scalability, and deployment in edge/cloud environments. **Key Requirements:** - 3 to 4 years of hands-on experience in data science with a focus on computer vision and ML projects. - Proficiency in Python and data science libraries like NumPy, Pandas, and Scikit-learn. - Expertise in image-based AI frameworks such as OpenCV, PyTorch, TensorFlow, Detectron2, YOLOv5/v8, and MMDetection. - Experience with generative AI models like GANs, Stable Diffusion, or ControlNet for image generation/augmentation. - Building and deploying ML models using tools like MLflow, TorchServe, or TensorFlow Serving. - Familiarity with image annotation tools (e.g., CVAT, Labelbox) and data versioning tools (e.g., DVC). - Proficiency in cloud platforms like AWS, GCP, or Azure for storage, training, or model deployment. - Experience with Docker, Git, and CI/CD pipelines for reproducible ML workflows. - Strong problem-solving skills, curiosity, and ability to work independently in a fast-paced environment. **Bonus / Preferred Skills:** - Experience in remote sensing and working with satellite/drone imagery. - Exposure to MLOps practices and tools like Kubeflow, Airflow, or SageMaker Pipelines. - Knowledge of solar technologies, photovoltaic systems, or renewable energy. - Familiarity with edge computing for vision applications on IoT devices or drones. Please note: No additional details of the company were provided in the job description.,