Asset Analytics Engineer

Capgemini Engineering is a global leader in engineering services, providing R&D and engineering services across industries. The company combines AI, technology, engineering and intelligent operations, serving enterprises worldwide with over 410,000 team members in more than 50 countries.

• Over 5 years of experience in data analytics, reliability engineering, or related fields
• Hands-on experience with platforms such as SmartSignal, Aspen Mtell, or AVEVA PRiSM including Blueprint and model review workflows
• Strong proficiency in Python (Pandas, NumPy) and solid SQL skills for querying CMMS systems (Maximo, SAP PM) and Historian databases
• Experience with industrial data systems including PI, OPC, and IP21 historians
• Familiarity with API data extraction tools such as Postman or equivalent
• Detail-oriented approach to data quality and model governance, managing model lifecycle activities including training, tuning, retraining, and performance monitoring

• Perform complex tag mapping of historian data sources (PI, OPC, IP21) to the SmartSignal Standard Data Model, ensuring data lineage, quality, and consistency across fleet-level deployment
• Apply Similarity-Based Modelling (SBM) and Empirical Model Learning (EML) techniques to establish accurate 'Normal' operating profiles
• Develop and maintain Analytic Blueprints as reusable templates for industrial asset classes such as pumps, motors, and transformers
• Monitor model performance using Precision/Recall metrics, retrain models after asset overhauls, and tune statistical thresholds to minimize false positives
• Write Python scripts (Pandas, NumPy) for data manipulation, custom analytic rule development, and feature engineering