Data Scientist

Roles & Responsibilities

• Analyze manufacturing plant and process data to identify patterns, anomalies, and optimization opportunities.
• Utilize the Seeq platform for: Time-series analysis; Root-cause investigations; Process monitoring and visualization
• Develop and deploy machine learning models for: Predictive maintenance; Process efficiency improvement; Quality and yield optimization
• Collaborate closely with plant, engineering, and operations teams to understand real-world process challenges.
• Translate business and operational requirements into data science solutions.
• Build and maintain data pipelines, analytical datasets, and workflows.
• Monitor, evaluate, and continuously improve model performance in production environments.
• Present actionable insights through dashboards, reports, and stakeholder discussions.
• Ensure data quality, reliability, and governance across manufacturing data sources.
• Drive the adoption of data-driven decision-making across plant operations.

Skills & Requirements

• 6+ years of experience in Data Science and Advanced Analytics.
• Hands-on experience in manufacturing, industrial, or plant environments.
• Strong working knowledge of Seeq (industrial analytics platform) for time-series analysis, including both Seeq Workbench and Seeq Data Lab (using the Seeq SPy library).
• Proficiency in Python (Pandas, NumPy, Scikit-learn) and SQL.
• Strong understanding of: Machine Learning (regression, anomaly detection, and predictive modeling); Statistical modeling and hypothesis-driven analysis; Time-series and sensor data analytics
• Experience building and deploying predictive models for: Predictive maintenance' Process optimization; Quality and yield improvement
• Ability to work with sensor data, process data, and operational datasets.
• Strong analytical thinking, troubleshooting, and root-cause analysis capabilities.

Good-to-Have Skills

• Experience in industries such as: Oil & Gas; Chemicals; Manufacturing
• Knowledge of MLOps, including model deployment, monitoring, and pipeline management.
• Exposure to optimization techniques for industrial processes.
• Exposure to cloud platforms such as Azure, AWS, or GCP.
• Familiarity with: Data visualization tools; Real-time and streaming data analytics; Data engineering concepts, including ETL, data pipelines, and data lakes