About this role
Must-Have Skills / Requirements
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6+ years of experience in Data Science / Advanced Analytics
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Hands-on experience in manufacturing / industrial / plant environments
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Strong working knowledge of Seeq (industrial analytics platform) for time-series analysis, including both Seeq Workbench and Data Lab (using the seeq spy library).
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Proficiency in Python (Pandas, NumPy, Scikit-learn) and SQL
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Strong understanding of:
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Machine Learning (regression, anomaly detection, predictive models)
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Statistical modeling and hypothesis-driven analysis
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Time-series / sensor data analytics
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Experience building and deploying predictive models for:
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Predictive maintenance
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Process optimization
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Quality and yield improvement
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Ability to work with sensor data, process data, and operational datasets
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Strong analytical thinking, troubleshooting, and root-cause analysis capability
Good-to-Have Skills
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Experience in industries such as:
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Oil & Gas, Chemicals, Manufacturing
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Knowledge of MLOps (model deployment, monitoring, pipelines)
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Exposure to optimization techniques for industrial processes
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Exposure to cloud platforms (Azure / AWS / GCP)
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Familiarity with data visualization tools, real-time / streaming data analytics, data engineering (ETL / data pipelines / data lakes)
Roles & Responsibilities
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Analyze manufacturing plant and process data to identify patterns, anomalies, and optimization opportunities
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Use Seeq platform for:
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Time-series analysis
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Root cause investigation
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Process monitoring and visualization
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Develop and deploy machine learning models for:
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Predictive maintenance
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Process efficiency improvement
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Quality / yield optimization
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Work closely with plant, engineering, and operations teams to understand real-world process issues
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Translate business and operational challenges into data science solutions
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Build and maintain data pipelines, analytical datasets, and workflows
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Monitor, evaluate, and continuously improve model performance in production
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Present actionable insights through dashboards, reports, and stakeholder discussions
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Ensure data quality, reliability, and governance across manufacturing data sources
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Drive adoption of data-driven decision-making across plant operations