Senior Data Scientist - Industrial Applications

Key Responsibilities

• Design, develop, and deploy end‑to‑end machine‑learning pipelines for predictive maintenance, quality control, and process optimization in an industrial environment.
• Apply deep learning and computer‑vision techniques to analyze sensor, image, and video data from manufacturing lines.
• Collaborate with domain experts and engineering teams to translate business problems into data‑science solutions and integrate models into production systems.
• Utilize AWS and Azure services (e.g., SageMaker, Azure ML, Data Lake, IoT Hub) for scalable model training, serving, and monitoring.
• Implement data‑engineering best practices, including data ingestion, cleaning, feature engineering, and versioning to ensure high‑quality datasets.

Requirements

• 5+ years of professional experience in data science or machine learning, preferably in an industrial or manufacturing context.
• Strong proficiency in Python and its data‑science ecosystem (pandas, NumPy, scikit‑learn, PyTorch/TensorFlow).
• Hands‑on experience with deep learning architectures for computer vision (CNNs, object detection, segmentation).
• Proven track record deploying models on cloud platforms such as AWS and Azure, including CI/CD pipelines and monitoring.
• Solid understanding of data engineering concepts, relational and time‑series databases, and big‑data processing frameworks.