About this role
Job Requirements Focus: Tag Mapping, Model Training, and Analytics Lifecycle Management Role Overview: The Analytics Engineer is responsible for the end-to-end technical deployment of predictive models. Leveraging the Smart Signal platform (or equivalent), you will transform raw historian data into high-fidelity digital twins. Your focus is on the "Digital Architecture" of reliability—ensuring models are accurate, noise-free, and scalable. Core Responsibilities:
• Data Orchestration & Tag Mapping: Perform complex mapping of historian tags (PI, OPC, IP21) to the SmartSignal Standard Data Model. Ensure data lineage and quality across fleet-level deployments. • Model Training (SBM): Utilize Similarity-Based Modeling (SBM) and Empirical Model Learning (EML) to establish "Normal" operating profiles. Select high-quality training windows (Gold Standard data) that represent healthy asset states. • Analytic Blueprinting: Develop and maintain "Analytic Blueprints" (templates) for common industrial classes such as pumps, motors, and transformers to enable rapid scaling. • Model Maintenance & Tuning: Monitor model performance (Precision/Recall). Perform "Retraining" following asset overhauls or upgrades and tune statistical thresholds to minimize false positives.
Work Experience Technical Skill Set:
• Programming: Proficient in Python for data manipulation (Pandas, NumPy) and building custom analytic rules/features. • Platform Expertise: Hands-on experience in SmartSignal (GE Vernova), Aspen Mtell, or AVEVA PRiSM. Deep understanding of "Blueprints" and "Weekly/Monthly Model Review" workflows. • Data Systems: Strong SQL skills for querying CMMS (Maximo, SAP PM) and Historian databases. • Software: Familiarity with pulling data from APIs using Postman or similar tools, ability to work efficiently big excel and csv files. • Strong analytical, debugging, and problem-solving skills. • Excellent verbal and written communication skills with the ability to work effectively in cross-functional teams. • Must have hands-on experience with Docker for containerizing, deploying, and managing applications. • Understanding of DevOps practices, including CI/CD pipelines and container based deployment strategies.