Risk Model Engineer – Pipeline Integrity

This is a fully remote position, open to applicants in United States, +1 more country.

📋 Description

• Transition current probabilistic model code to the updated risk management platform

• Verify results against existing production implementations

• Incorporate model inputs from various datasets across Irth’s range of products

• Broaden threat coverage across transmission pipeline integrity areas, including corrosion, cracking, third-party damage, and external forces

• Create and develop time-series, anomaly detection, and geospatial models utilizing inspection, sensor, maintenance, and related datasets

• Execute industry-standard probabilistic risk models within the analytical framework

• Facilitate customer-driven model development on the platform

• Generate, assess, and present sensitivity analyses for model offerings

• Confirm model outputs against customer data, known outcomes, and regulatory standards

• Collaborate daily with ML Ops and data engineering teams while overseeing model methodology, content, validation, and supporting evidence

⛳️ Requirements

• Practical experience in North American oil and gas transmission is of utmost importance

• Familiarity with North American oil and gas transmission integrity management

• Understanding of relevant standards and regulations such as ASME B31.8S, 49 CFR 192, CSA Z662, or their equivalents

• Knowledge of fitness-for-service, remaining-strength assessments, corrosion growth modeling, or fracture mechanics

• Experience with in-line inspection (ILI) vendor data formats, data alignment, anomaly classification, and feature disposition

• Background in high-consequence areas, class-location analysis, or related pipeline risk assessment requirements

• Proven ability to prepare technical documentation and validation evidence for regulatory review, audits, or formal assessments

• Experience in constructing or validating risk, reliability, or failure models for physical infrastructure

• Acquaintance with Monte Carlo simulation, structural reliability, uncertainty quantification, or related probabilistic methodologies

• Experience applying machine learning techniques to time-series, survival, anomaly detection, or classification challenges involving physical asset data

• Valuable experience in training and validating models on sparse, imbalanced failure events

• Familiarity with ArcGIS, PostGIS, linear referencing, or geospatial risk assessment

• Proficiency in Python and solid software engineering practices, including testing, code reviews, reproducibility, and disciplined use of AI coding assistants

• Comfort with incomplete, inconsistent, and imperfect data

• Ability to differentiate between a defensible, validated model and one that merely produces a number

🏝️ Benefits

• Competitive compensation package based on experience and qualifications

• Medical, Dental, and Vision Insurance

• 401(k) Plan with Company Match

• Generous Paid Time Off (PTO)

• Company-Paid Holidays

• Flexible Work Options / work-from-home opportunities

• On-Call Compensation for eligible on-call shifts