Risk Model Engineer - Pipeline Integrity (Transmission)
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Irth SolutionsPipeline integrity
Remote within the United States or Canada, Working hours within approximately four hours of Central TimeFull-Time
Salary not disclosed
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Job Details
- Required Skills
- PythonMachine Learning
Requirements
- Experience with North American oil and gas transmission pipeline integrity management, threat identification, risk assessment, and integrity mitigation or remediation prioritization.
- Familiarity with transmission pipeline threats such as external and internal corrosion, cracking, and third-party damage.
- Experience developing, implementing, migrating, or validating probabilistic models for pipeline integrity or asset risk.
- Familiarity with in-line inspection (ILI), pipeline inspection records, sensor data, maintenance histories, or related operational datasets.
- Experience evaluating model assumptions, uncertainty, sensitivity, and the impact of risk factors on model outputs.
- Experience supporting regulatory reviews, integrity assessments, audits, or customer decisions with defensible technical evidence.
- Experience applying machine learning to physical asset data, including time-series analysis, anomaly detection, classification, or survival analysis.
- Experience with spatial data, GIS tools, or geospatial risk assessment.
- Python skills and sound software engineering practices, including testing, code review, and reproducibility.
- Comfort working with incomplete or inconsistent data and judging whether models are defensible and validated.
- Working familiarity with both pipeline integrity and quantitative modeling; candidates may bring deeper expertise in either area.
Responsibilities
- Migrate existing probabilistic model code to the new risk management platform.
- Validate model results against the current production implementation.
- Integrate model inputs from datasets across Irth’s product offerings.
- Extend threat coverage across transmission pipeline integrity domains, including corrosion, cracking, third-party damage, and outside forces.
- Design and build time-series, anomaly detection, and geospatial models using inspection, sensor, maintenance, and related data.
- Implement industry-standard probabilistic risk models and support customer-driven model development on the platform.
- Develop, evaluate, and present sensitivity analyses for model offerings.
- Validate model outputs against customer data, known outcomes, and applicable regulatory expectations.
- Partner with ML Ops and data engineering teams on model methodology, content, validation, and supporting evidence.
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