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.
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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.
