TIMP Model Engineer

Risk Model Engineer — Pipeline Integrity (Transmission)

Skills We’re Hiring For

We’re hiring for the capabilities below, but we do not expect a single candidate to have all of them. Deep expertise in one or more areas, combined with the ability to learn and collaborate across the others, is what matters.

• Experience building or validating quantitative risk, reliability, or failure models for physical infrastructure. Pipeline experience is ideal; experience with rail, structural, water, electric, or other utility assets transfers well.
• Familiarity with pipeline integrity standards and regulations, such as ASME B31.8S, 49 CFR 192, CSA Z662, or equivalent frameworks.
• Experience with fitness-for-service and remaining-strength assessment, corrosion growth modeling, or fracture mechanics.
• Experience working with inline inspection (ILI) data, including vendor formats, data alignment, anomaly classification, and feature disposition.
• Experience with Monte Carlo simulation, structural reliability, and uncertainty quantification.
• Experience applying machine learning to physical asset data, including time-series, survival, anomaly detection, and classification models trained and validated against sparse or imbalanced events.
• Experience with consequence-area and class-location analysis.
• Geospatial analysis experience, including tools and technologies such as ArcGIS, PostGIS, or linear referencing.
• Experience producing model validation evidence suitable for regulatory, audit, customer, or other formal review.
• Strong software engineering practices, including testing, code review, reproducibility, and disciplined use of AI-assisted development tools.

Success Metrics

You’ll know you’re succeeding when:

• Existing models are running on the new platform with validated parity against the current implementation.
• New threat models take advantage of the integration of Irth datasets to produce meaningful analytical capabilities.
• Customer-built models are successfully implemented, validated, and supported on the platform.
• Validation evidence stands up to design-partner and regulatory review, with every model output reproducible and traceable.
• You collaborate effectively with data engineering, ML Ops, data science, and subject-matter experts to move models from concept through validated implementation.