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:
- 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
