Risk Model Engineer - Gas Distribution

Employment: Full-time Experience: Mid-Senior level Education: Master's Degree Risk Model Engineer — Gas Distribution Integrity Location: Remote (US or Canada) Department: Insights (AI/ML) Reports to: Data Science Team Lead About Irth Solutions If you want your work to make infrastructure smarter, safer, and more resilient, you’ll thrive here. 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 directly shape how utilities understand and prioritize risk across their gas distribution systems. You’ll help turn fragmented asset data, engineering knowledge, and advanced analytics into defensible decisions that improve infrastructure safety and reliability. You’ll also help establish the analytical foundation of a new AI-driven platform, with the opportunity to influence its models, threat coverage, and customer-facing capabilities from the ground up. Work Location This is a remote position open to candidates based in the United States or Canada, with working hours that overlap within approximately four hours of Central Time. Our Hiring Approach We’re hiring two Risk Model Engineers and intentionally balancing industry expertise with analytical depth. Deep gas distribution experience is especially valuable, and extensive modeling experience is not a prerequisite for every candidate. We’re interested in candidates who bring strong practical knowledge, strong quantitative skills, or a combination of both. Requirements What Will Help You Succeed Gas distribution integrity management: Experience with program design, threat identification under 49 CFR 192 Subpart P, and asset replacement prioritization. Experience from a gas utility, consultancy, regulatory agency, or industry vendor is valuable. Legacy material risk: Familiarity with the risks associated with cast iron, bare steel, vintage plastics, and plastic embrittlement. Leak and threat detection programs: Experience with leak surveys, methane detection, or cross-bore identification and mitigation programs. Regulatory and rate-case support: Exposure to regulatory filings, rate cases, or the development of validation evidence for regulatory or audit review. Distribution integrity tools: Hands-on experience with established gas distribution integrity management tools and an understanding of their strengths and limitations. Machine learning and risk modeling: Experience applying machine learning to physical asset data, including time-series analysis, survival analysis, anomaly detection, or classification. Familiarity with training and validating models on sparse, imbalanced failure data is particularly valuable. Geospatial analysis: Experience with tools and technologies such as ArcGIS, PostGIS, and spatial risk scoring. Python and software engineering: Sound software engineering practices, including testing, code review, and disciplined use of AI coding assistants. Real-world data and model judgment: Comfort working with incomplete, inconsistent, and imperfect data, with the judgment to distinguish a defensible, validated model from one that simply produces a number. You don’t need to check every box. We’re particularly interested in candidates who bring deep practical gas distribution knowledge or strong quantitative modeling expertise and can work effectively across both domains. Benefits Competitive Salary – A competitive compensation package based on experience and qualifications. Medical, Dental, and Vision Insurance – Comprehensive insurance coverage to support you and your family. 401(k) Plan with Company Match. Generous Paid Time Off (PTO) – Time off to support work-life balance and personal needs. Company-Paid Holidays – Paid holidays throughout the year. Flexible Work Options – Work-from-home opportunities are available, depending on role and business needs. On-Call Compensation – Additional pay for eligible on-call shifts.