Senior Data Scientist

Proficient in Python and ML libraries such as scikit-learn, XGBoost, TensorFlow, or similar.
Hands-on experience with time-series modeling, anomaly detection, and condition-based monitoring applications.
Familiarity with manufacturing KPIs, equipment telemetry, and industrial datasets (vibration, temperature, cycle time, etc.).
Experience with cloud-based ML pipelines and MLOps practices (Azure, AWS, or other).
Excellent communication and problem-solving skills in a cross-functional manufacturing environment.
Bachelor’s or master’s degree in data science, Computer Engineering, Mechanical Engineering, or a related field.
6+ years of experience building and deploying machine learning models in production environments.

Develop machine learning models to predict downtime, detect process anomalies, and reduce quality risk based on operational and sensor data.
Analyze and model time-series data from PLCs, historians, SCADA, and IoT platforms.
Collaborate with Data Engineers and IoT Developers to ensure availability, structure, and integrity of real-time data.
Work directly with plant operations and engineering teams to identify predictive use cases and validate model outcomes.
Deploy and monitor models in production environments (cloud or edge) with appropriate feedback and retraining cycles.
Document model development, performance, assumptions, and dependencies for traceability and knowledge transfer.
Partner with leadership to assess business value and model ROI in predictive maintenance and operational improvement use cases.
Stay current with best practices and advancements in AI/ML, particularly within manufacturing applications.