Data Scientist - Manufacturing Analytics
Summary
Build and deploy ML models for predictive maintenance, process optimization, and quality improvement in manufacturing plants using Python, SQL, and Seeq.
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:
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:
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:
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:
Time-series analysis
Root cause investigation
Process monitoring and visualization
Develop and deploy machine learning models for:
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