Industrial IoT Specialist - Predictive Analytics and Equipment Health Monitoring

Specialist - Industrial IoT Key Responsibilities Understand customer business and reliability objectives and translate them into Industrial AI predictive analytics use cases Analyze PIDs PFDs equipment design operating philosophy and performanceefficiency calculations to understand asset processes operating boundaries and failure behavior Define and validate assettotag relationships ensuring relevant process mechanical and performance parameters are correctly mapped from DCS SCADA historians and other data sources Work with AIML and predictive analytics platforms to configure deploy and operationalize models for equipment health and performance monitoring Apply engineering domain knowledge to interpret model outputs anomalies patterns correlations and early warnings and assess their operational relevance Perform functional validation and quality assurance of analytics models by comparing model behavior with equipment operating conditions historical patterns and known failure mechanisms Support the development of AIMLdriven monitoring approaches by providing equipmentdomain knowledge failuremode understanding and relevant featuretag selection Collaborate with Product Project Management Data Science Engineering and Technical Consulting teams to ensure solutions meet customer requirements Identify opportunities to improve model effectiveness monitoring workflows automation and deployment scalability Develop reusable domain knowledge validation methodologies equipment templates and best practices across predictive analytics implementations Qualifications Functional Skills Industrial Domain Experience Strong understanding of rotating andor process equipment operating principles performance characteristics and common failure mechanisms Predictive Maintenance Reliability Working knowledge of condition monitoring predictive maintenance RCA FMEA RCM and equipment health assessment AIML Familiarity Good understanding of AIML concepts relevant to industrial applications including anomaly detection regression classification pattern recognition timeseries analytics model training validation and performance evaluation Predictive Analytics Platforms Familiarity with industrial analyticsAPM platforms such as GE SmartSignal AVEVA PRiSM Aspen Mtell UptimeAI Prometheus APM Bently Nevada System1 or similar solutions Engineering Documentation Ability to interpret PIDs PFDs equipment datasheets control philosophy process calculations and performance curves Tag Mapping Data Understanding Ability to identify and map relevant sensorprocess tags from DCS SCADA PLCs and historians to analytics models Model Validation Ability to evaluate whether model outputs and detected anomalies are technically meaningful and consistent with actual equipmentprocess behavior AIML Implementation Exposure 2 years of experience working with analytics solutions AIMLbased monitoring predictive models or rulebased diagnostic solutions for rotating machines in industrial environments Crossfunctional Collaboration Ability to bridge the gap between domain engineering customer requirements data science and technology teams