Sr. Data Science Engineer

Job Description

Key Responsibilities Design and develop machine learning models for computer vision tasks including object detection, classification, tracking, and scene understanding. Lead end-to-end model lifecycle: data preprocessing, feature engineering, model selection, training, evaluation, and deployment. Implement and experiment with state-of-the-art techniques including Vision Transformers (ViT) and class-incremental learning for continuous adaptation in production environments. Analyze data from edge devices and physical environments to generate actionable insights. Develop and maintain reproducible research pipelines using versioned data and experiment tracking tools. Collaborate with the ML Ops and engineering teams to ensure seamless integration of models into production environments. Optimize models for real-time inference performance, accuracy, and scalability on edge devices or cloud-based infrastructure. Document findings, present insights to cross-functional teams, and contribute to technical decision-making. Mentor junior data scientists and contribute to team knowledge sharing. Required Qualifications 4+ years of experience in data science or machine learning roles, with hands-on production experience. Expertise in computer vision algorithms and deep learning models.\ Strong grasp of cutting-edge architectures such as CNNs , Vision Transformers (ViT) , and knowledge distillation techniques. Practical experience with class-incremental learning , transfer learning , and domain adaptation . Proficiency in Python and ML libraries such as PyTorch , TensorFlow , scikit-learn , and OpenCV . Hands-on experience with data versioning and experiment tracking tools such as DVC , MLflow etc. Solid understanding of ML model deployment and lifecycle management. Strong experience in working with structured and unstructured data, especially large-scale image/video datasets. Excellent analytical skills and the ability to interpret complex data and communicate results clearly. Preferred Qualifications Prior experience in startup or high-growth environments where adaptability is key. Experience working with cloud platforms such as AWS , GCP , or Azure for scalable ML workflows. Familiarity with edge computing , real-time inference , and model optimization frameworks like TensorRT , ONNX , or OpenVINO . Experience with CI/CD pipelines for ML projects and collaboration with ML Ops teams. Domain knowledge in retail, healthcare, logistics, or behavioral analytics is a strong plus. Contributions to open-source ML projects or published research papers. Comfort with visualization tools and reporting frameworks for data storytelling.