Data Scientist - Manufacturing Analytics

Description Must-Have Skills / Requirements

• 6+ years of experience in Data Science / Advanced Analytics

• Hands-on experience in manufacturing / industrial / plant environments

• Strong working knowledge of Seeq (industrial analytics platform) for time-series analysis, including both Seeq Workbench and Data Lab (using the seeq spy library).

• Proficiency in Python (Pandas, NumPy, Scikit-learn) and SQL

• Strong understanding of:

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• Machine Learning (regression, anomaly detection, predictive models)

• Statistical modeling and hypothesis-driven analysis

• Time-series / sensor data analytics

• Experience building and deploying predictive models for:

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• 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 capability

Good-to-Have Skills

• Experience in industries such as:

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• Oil & Gas, Chemicals, Manufacturing

• Knowledge of MLOps (model deployment, monitoring, pipelines)

• Exposure to optimization techniques for industrial processes

• Exposure to cloud platforms (Azure / AWS / GCP)

• Familiarity with data visualization tools, real-time / streaming data analytics, data engineering (ETL / data pipelines / data lakes)

Roles & Responsibilities

• Analyze manufacturing plant and process data to identify patterns, anomalies, and optimization opportunities

• Use Seeq platform for:

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• Time-series analysis

• Root cause investigation

• Process monitoring and visualization

• Develop and deploy machine learning models for:

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• Predictive maintenance

• Process efficiency improvement

• Quality / yield optimization

• Work closely with plant, engineering, and operations teams to understand real-world process issues

• Translate business and operational challenges into data science solutions

• Build and maintain data pipelines, analytical datasets, and workflows

• Monitor, evaluate, and continuously improve model performance in production

• Present actionable insights through dashboards, reports, and stakeholder discussions

• Ensure data quality, reliability, and governance across manufacturing data sources

• Drive adoption of data-driven decision-making across plant operations