Risk Model Engineer - Gas Distribution
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Irth SolutionsGas distribution
Remote position open to candidates based in the United States or Canada, Working hours that overlap within approximately four hours of Central Time.Full-Time
Salary not disclosed
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Job Details
- Required Skills
- PythonMachine Learning
Requirements
- Practical experience with North American gas distribution systems, including asset integrity, pipeline risk, operations, engineering, or a closely related field.
- Understand gas distribution threats, asset characteristics, and operational factors influencing risk and replacement decisions.
- Translate industry knowledge and risk frameworks into structured, defensible analytical models.
- Apply strong analytical judgment and validate model assumptions, outputs, and limitations.
- Collaborate with subject-matter experts, data scientists, ML Ops, and data engineering teams.
- Explain model results and risk-based recommendations to technical and nontechnical stakeholders.
- Experience with gas distribution integrity management, threat identification under 49 CFR 192 Subpart P, and asset replacement prioritization is valuable.
- Familiarity with legacy material risks, including cast iron, bare steel, vintage plastics, and plastic embrittlement.
- Experience with leak surveys, methane detection, or cross-bore identification and mitigation programs is valuable.
- Helpful experience includes probabilistic risk modeling, machine learning, time-series analysis, anomaly detection, geospatial modeling, and model validation.
Responsibilities
- Translate gas distribution threat frameworks into systematic threat identification methods and supporting data structures.
- Integrate model inputs from diverse datasets across Irth’s product offerings.
- Expand threat coverage across legacy material corrosion, plastic embrittlement, excavation damage, cross-bores, and other distribution threats.
- Design and build time-series, anomaly detection, and geospatial models using leak survey, methane detection, maintenance, and related data.
- Implement industry-standard distribution risk methodologies in the analytical layer and support customer-driven model development.
- Develop and present sensitivity analyses for model offerings.
- Validate model outputs against customer data, known outcomes, and regulatory requirements.
- Produce system-wide risk rankings and mitigation rationales for asset replacement programs and regulatory rate cases.
- Partner with ML Ops and data engineering teams while owning model methodology, content, validation, and supporting evidence.
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