Predictive Asset Analytics Engineer

Capgemini Engineering; Województwo pomorskie; Hybrid; PLN 180,000 - 300,000; Full time

Job summary

Capgemini Engineering seeks an Asset Analytics Engineer to own end-to-end deployment of predictive models for industrial asset fleets, leveraging platforms like SmartSignal, Aspen Mtell, and AVEVA PRiSM. You will transform historian data into high-fidelity digital twins to support reliability decisions.

The role requires strong Python (Pandas, NumPy) and SQL skills, experience with PI/OPC/IP21 historians, and a structured approach to data quality and governance.

Qualifications

• 5+ years of experience in data analytics, reliability engineering, or a related field, with hands-on experience on platforms such as SmartSignal, Aspen Mtell, or AVEVA PRiSM, including Blueprint and model review workflows
• Strong Python proficiency (Pandas, NumPy) for data manipulation and custom analytics development, combined with solid SQL skills for querying CMMS systems (Maximo, SAP PM) and Historian database
• Demonstrated experience with industrial data systems including PI, OPC, and IP21 historians, and familiarity with API data extraction tools such as Postman or equivalent
• A structured, detail-oriented approach to data quality and model governance, with the ability to manage model lifecycle activities including training, tuning, retraining, and performance monitoring across large asset fleets

Responsibilities

• Performing complextag mapping of historian data sources (PI, OPC, IP21) to the SmartSignal Standard Data Model, ensuring data lineage, quality, and consistency across fleet-level deployment
• Applying SBM and EML techniques to establish accurate 'Normal' operating profiles, selecting Gold Standard training windows that represent healthy asset state
• Developing and maintaining Analytic Blueprints—reusable templates for common industrial asset classes such as pumps, motors, and transformers - to enable rapid and scalable deployment
• Monitoring model performance using Precision/Recall metrics, performing retraining following asset overhauls, and tuning statistical thresholds to minimise false positives and maximise signal fidelity
• Writing Python scripts(Pandas, NumPy) for data manipulation, custom analytic rule development, and feature engineering to enhance model accuracy

Skills

Python (Pandas, NumPy)

SQL

Data analytics

Reliability engineering

Tools

SmartSignal

Aspen Mtell

AVEVA PRiSM

Maximo

SAP PM

PI/OPC/IP21 historians

Postman

Job description

Capgemini Engineering seeks an Asset Analytics Engineer to own end-to-end deployment of predictive models for industrial asset fleets, leveraging platforms like SmartSignal, Aspen Mtell, and AVEVA PRiSM. You will transform historian data into high-fidelity digital twins to support reliability decisions.

The role requires strong Python (Pandas, NumPy) and SQL skills, experience with PI/OPC/IP21 historians, and a structured approach to data quality and governance.