Global Predictive Maintenance Expert

Context: The candidate will join the Engineering & Sites & Facilities organization within the Physical Asset Management function, supporting the deployment of a global AI-driven predictive and prescriptive maintenance strategy across manufacturing, laboratories, utilities, facilities and capital projects. This newly created role aims to establish capabilities that are currently not available internally, combining maintenance expertise with advanced digital technologies, machine learning and predictive analytics. The candidate will act as a global subject matter expert and will help shape how artificial intelligence, industrial data and analytics can be leveraged to improve asset reliability, maintenance effectiveness and maintenance decision-making across the organisation. The role sits at the intersection of Engineering, Maintenance, Data, Digital Technology and Artificial Intelligence. It does not focus solely on traditional predictive maintenance techniques (vibration analysis, thermography, lubrication analysis, etc.). Candidates are expected to demonstrate hands-on experience leveraging AI, machine learning and advanced analytics to enable predictive and prescriptive maintenance decision-making. Team info: The candidate will join a multi-site international team operating across Belgium and Switzerland. The core team consists of approximately five long-term external experts, complemented by additional specialists depending on project needs. The role requires close collaboration with engineering teams, digital technology teams, data specialists, reliability engineers, asset owners, project teams and external technology providers. The candidate must be comfortable interacting with both technical and business stakeholders and influencing decisions without formal authority. Day to day:
· Define and deploy predictive and prescriptive maintenance strategies, standards, governance models and roadmaps across all sites.
· Develop and maintain predictive maintenance methodologies, including asset selection criteria, monitoring strategies, alarm governance and lifecycle requirements.
· Assess asset criticality, failure modes and business impact to determine the most appropriate predictive and prescriptive maintenance approaches.
· Provide expertise in condition monitoring technologies including vibration analysis, thermography, ultrasound, lubrication analysis, motor and electrical monitoring, process analytics and other predictive maintenance techniques.
· Evaluate, assess and support the deployment of AI-driven maintenance solutions, machine learning models, predictive analytics platforms and digital twin technologies.
· Collaborate with Digital Technology teams on industrial data architectures, IoT ecosystems, data lakes, data warehouses and connected asset environments required to support predictive and prescriptive maintenance capabilities.
· Lead technical assessments, proofs of concept and deployment initiatives with internal and external stakeholders.
· Define predictive maintenance and digital asset management requirements for new facilities and capital projects through Reliability by Design principles.
· Integrate predictive maintenance outputs, predictive analytics and AI-generated insights into CMMS/EAM systems, maintenance workflows and asset strategies.
· Perform diagnostics and translate condition monitoring, reliability and asset-performance data into actionable maintenance recommendations.
· Develop KPI frameworks, dashboards and value-tracking methodologies linked to reliability, asset performance, maintenance optimization and risk reduction.
· Build and support communities of practice, training programs and competency development initiatives related to predictive and prescriptive maintenance.
· Participate in reporting, presentations and stakeholder engagement activities to drive adoption of predictive maintenance, advanced analytics and AI-enabled maintenance practices.
· Act as a bridge between Engineering, Maintenance, Reliability, Data and Digital Technology teams to ensure successful deployment of predictive and prescriptive maintenance solutions.
· Approximately 50% of the role is highly technical and 50% focuses on stakeholder management, communication, change management and organizational adoption. Requirements
· Master’s degree in Mechanical, Electrical, Industrial, Automation, Data or Reliability Engineering, or an equivalent qualification.
· Hands-on experience with AI-driven predictive and/or prescriptive maintenance solutions.
· Experience applying Machine Learning, predictive analytics or advanced data-driven methodologies to maintenance use cases.
· Strong understanding of industrial data architectures, IoT ecosystems, data lakes, data warehouses and connected asset environments.
· Knowledge of cGMP, Good Engineering Practices, EHS requirements and risk-based approaches within regulated environments.
· Experience in the pharmaceutical, biotechnology or a closely comparable regulated industrial environment. Petrochemical experience may be considered when the candidate demonstrates highly relevant predictive-maintenance expertise and a strong understanding of regulated ways of working.
· Experience evaluating, implementing or governing predictive analytics, AI or digital maintenance platforms.
· Ability to bridge Maintenance, Engineering, Data and IT teams.
· Strong understanding of predictive maintenance and condition-based maintenance methodologies.
· Knowledge of reliability engineering principles such as FMECA, RCM, RCA and asset criticality analysis.
· Experience with CMMS/EAM platforms and integration of maintenance data into business processes.
· Strong stakeholder management and communication skills.
· Ability to work autonomously in a global, multi-site environment.
· Ability to translate business and asset risks into technical requirements, implementation roadmaps and quantified value cases.
· Strong analytical and structured problem-solving capabilities, sound judgment under uncertainty and a pragmatic deployment mindset.
· Ability to work independently and deliver expertise that is not currently available internally.
· Professional proficiency in English.
· Willingness to travel internationally when required for technology evaluations, projects and site deployments. Nice to have:
· Experience in maintenance-organization design or broader maintenance-service models.
· Experience with Digital Twin technologies.
· Experience with Asset Performance Management (APM) platforms.
· Relevant certifications such as CMRP, CRL, vibration analysis ISO 18436, thermography, ultrasound, lubrication analysis, IAM Certificate or Diploma, Lean Six Sigma or data analytics.
· Experience supervising strategic technology suppliers and defining expectations regarding competencies, deliverables, data ownership and service quality.
· French or another language used across all sites.
· Experience building global communities of practice and training programs.