Irth Solutions builds advanced technology that empowers the people who keep our lights on, water running, and pipelines flowing. Our tools improve the safety, reliability, and efficiency of critical infrastructure and the field technicians, engineers, and analysts who operate it.

We’re building a new AI-driven threat and risk management platform for pipeline asset integrity, and we’re looking for a Risk Model Engineer to own the gas distribution risk models that power its analytical layer.

In this role, you’ll help define, build, and validate models that utilities use to prioritize asset replacement, reduce risk, and support regulatory decisions. You’ll work across physics-based probabilistic models and machine learning, analyzing large populations of buried assets where observed failures are rare and asset records are often incomplete.

Practical experience in North American gas distribution is our top priority. We encourage strong industry candidates to apply even if their modeling experience is less extensive. We’re hiring two engineers and will balance the team’s strengths, with one potentially bringing deeper industry expertise and the other deeper modeling expertise.

What You’ll Do

Translate gas distribution threat frameworks into systematic threat identification methods and the data structures needed to support them, partnering with subject-matter experts.

Integrate model inputs from diverse datasets across Irth’s product offerings.

Expand threat coverage across areas such as 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 within the analytical layer and support customer-driven model development on the platform.

Develop and present robust sensitivity analyses for all model offerings.

Validate model outputs against customer data, known outcomes, and regulatory requirements.

Produce system-wide risk rankings with clear mitigation rationales to support asset replacement programs and regulatory rate cases.

Partner daily with ML Ops and data engineering teams, who own productionization, while you own the model methodology, content, validation, and supporting evidence.

What We’re Looking For

Practical experience with North American gas distribution systems, including asset integrity, pipeline risk, operations, engineering, or a closely related field.

An understanding of gas distribution threats, asset characteristics, and the operational factors that influence risk and replacement decisions.

The ability to translate industry knowledge and risk frameworks into structured, defensible analytical models.

Strong analytical judgment and a commitment to validating model assumptions, outputs, and limitations.

The ability to work collaboratively with subject-matter experts, data scientists, ML Ops, and data engineering teams.

Strong communication skills, including the ability to explain model results and risk-based recommendations to technical and nontechnical stakeholders.

Helpful Additional Experience

Physics-based, probabilistic, or quantitative risk modeling.

Machine learning applied to infrastructure, asset integrity, or other complex systems.

Time-series analysis, anomaly detection, or geospatial modeling.

Working with large, incomplete, or inconsistent asset datasets where observed failures are limited.

Sensitivity analysis, model validation, and uncertainty quantification.

Familiarity with regulatory scrutiny, utility replacement programs, and rate case support.

Why This Role Matters

Your work will…