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analytics\nExperience building and deploying predictive models for: \nPredictive maintenance\nProcess optimization\nQuality and yield improvement\nAbility to work with sensor data, process data, and operational datasets\nStrong analytical thinking, troubleshooting, and root-cause analysis capability\nGood-to-Have Skills\nExperience in industries such as: \nOil & Gas, Chemicals, Manufacturing\nKnowledge of MLOps (model deployment, monitoring, pipelines)\nExposure to optimization techniques for industrial processes\nExposure to cloud platforms (Azure / AWS / GCP)\nFamiliarity with data visualization tools, real-time / streaming data analytics, data engineering (ETL / data pipelines / data lakes)\nRoles & Responsibilities\nAnalyze manufacturing plant and process data to identify patterns, anomalies, and optimization opportunities\nUse Seeq platform for:\nTime-series analysis\nRoot cause investigation\nProcess monitoring and visualization\nDevelop and deploy machine learning models for:\nPredictive maintenance\nProcess efficiency improvement\nQuality / yield optimization\nWork closely with plant, engineering, and operations teams to understand real-world process issues\nTranslate business and operational challenges into data science solutions\nBuild and maintain data pipelines, analytical datasets, and workflows\nMonitor, evaluate, and continuously improve model performance in production\nPresent actionable insights through dashboards, reports, and stakeholder discussions\nEnsure data quality, reliability, and governance across manufacturing data sources\nDrive adoption of data-driven decision-making across plant 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