Competencies of an AI, Automation & Data Specialist

By Imelda Kehoe, Technical & OEE Partners Ltd

AI-Driven Predictive Maintenance | Process Optimisation | Industrial Analytics

Senior Data Scientists have experience applying advanced analytics and machine learning within industrial and regulated manufacturing environments. Specialises in predictive maintenance, anomaly detection, and process optimisation, with a pragmatic approach aligned to GxP and validated system constraints.

Core Competencies

• Predictive Maintenance & Condition Monitoring
• Machine Learning & Statistical Modelling
• Time-Series and Event-Based Analytics
• Manufacturing Process Optimisation
• OEE and Performance Analytics
• Industrial Data Integration (OT/IT)

Technical Expertise

• The should demonstrate proven expertise in:
• Supervised and unsupervised machine learning models
• Time-series forecasting and anomaly detection
• Root cause analysis using multivariate data
• Strong hands-on capability with:
• Programming languages such as C++, Python
• SQL and industrial data historians
• Data visualisation and analytics dashboards
• Experience designing analytics pipelines using:
• Edge and cloud-based architectures
• Secure industrial data frameworks

Industrial & Automation Integration

• Extensive experience integrating analytics solutions with:
• PLC and IPC-based automation systems
• SCADA, MES, and OEE platforms
• OPC-UA and industrial communication protocols
• Works with diverse data sources, including:
• Sensor data (vibration, temperature, current, torque)
• Vision system outputs
• Alarm, event, and batch data
• Maintenance and downtime records

Regulated Environment Expertise

• Strong understanding of GxP constraints and regulatory expectations.
• Designs AI and analytics solutions that:
• Do not impact validated system states
• Respect data integrity and segregation requirements
• Align with GAMP5 lifecycle and change control principles
• Experienced in defining GMP vs non-GMP data boundaries and validation strategies for analytics platforms.

Business & Operational Impact

• Demonstrated success delivering:
• Reduction in unplanned downtime
• Improved asset reliability and OEE
• Early fault detection and predictive alerts
• Data-driven maintenance strategies
• Translates complex analytical outputs into:
• Actionable engineering insights
• Operator-friendly dashboards
• Management-level performance metrics

Consulting & Stakeholder Engagement

• Proven ability to operate across:
• Engineering
• IT / OT
• Quality and Validation
• Senior leadership teams

By: Imelda Kehoe