Job 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:
• 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