Data Scientist

This role focuses on turning complex manufacturing data into clear, actionable insights using modern analytics tools such as Python and machine learning. It supports 60+ plants globally and collaborates with Manufacturing Execution, Process Excellence, Engineering, and Sourcing teams to drive productivity, optimize costs, and enable data-driven decision making across the organization.

• Master or bachelor degree in Engineering, IT or Data & Computer Science.
• Fluency in English; any other language is an asset.
• 3+ years experience with Python as a data scientist, with proficiency in data exploration, machine learning, and visualization.
• Preferably 1+ year exposure to industrial data from processing or manufacturing industry or power plants.
• Experience with databases / SQL required.
• Experience with Python in machine learning required.
• Experience with data visualization / reporting tools (PowerBI / SSRS / Tableau / SAC).
• Basic understanding of manufacturing automation (PLC / SCADA / MES / Historian / OSI PI).
• Demonstrated experience in data analysis, KPI development, and reporting.
• Strong understanding of maintenance operations and asset management principles.
• Reliability and/or maintenance management background is highly desirable.
• Experience with CMMS (Computerized Maintenance Management System) is highly desirable.
• Excellent analytical, problem-solving, and critical thinking skills.
• Ability to communicate complex data insights clearly to both technical and non-technical audiences.

• Deliver actionable insights by extracting, analyzing, and interpreting data from CMMS, MES, and other operational systems.
• Identify trends in MRO data and translate findings into clear reports, dashboards, and benchmarks aligned with maintenance strategies.
• Lead global spare-parts data cleansing and harmonization, optimize inventory levels, and coordinate removal of obsolete or duplicate materials.
• Support initiatives such as VMI and consignment stock.
• Develop and maintain the CMMS and its master data, ensuring strong governance, data quality, and alignment with KPIs and operational objectives.
• Act as a global super user, improve workflows, support CMMS roadmap deployment, and create training content to strengthen site capabilities.
• Manage master data for equipment, spare parts, and services.
• Promote data democratization by enabling self-service analytics through Power BI.
• Maintain and improve datasets, methodologies, and documentation to support informed decision-making across maintenance teams.
• Continuously enhance analytics processes and tools, working closely with maintenance, IT, and digital teams to resolve data issues, improve data quality, and ensure accuracy, accessibility, and relevance.
• Apply standardized reporting principles, including SQL stored procedures, global reporting tools, change management practices, and documentation standards.