Data Scientist

Role & responsibilities

Collaborate with operations teams to understand data sources including ERP systems, production logs, quality data, and equipment sensors.
• Extract, clean, validate, and consolidate data from multiple sources for analysis.
• Perform exploratory data analysis to identify patterns, anomalies, and opportunities for manufacturing yield improvement.
• Build analytical and statistical models to identify factors impacting quality, efficiency, and operational performance.
• Apply data science techniques such as regression, classification, trend analysis, and predictive modeling using Python/R and SQL.
• Partner with manufacturing leadership to define business problems and translate them into analytical solutions.
• Develop dashboards and visualizations using Power BI, Tableau, or similar tools to support decision-making.
• Present analytical insights and recommendations to business stakeholders with a focus on measurable impact.
• Build predictive models for equipment maintenance, quality prediction, and process optimization.
• Work closely with IT teams to establish robust data pipelines and reporting frameworks.
• Develop standardized approaches for data extraction, data quality management, and analytics.
• Document methodologies, models, and analytical processes while ensuring maintainability and scalability.
• Identify data gaps and recommend improvements to data collection and operational reporting systems.
• Conduct knowledge-sharing sessions and training programs to promote data-driven decision-making across the organization.