Description

Build predictive models and quantitative risk frameworks for transmission rights of way to assess safety, reliability, and operational risks.

This role is on-site.

Responsibilities

• Develop risk equations and analytical models estimating event likelihood and consequence across safety, reliability, and financial dimensions.
• Apply machine learning techniques including logistic regression, survival analysis, random forests, and Bayesian methods to predict future incidents.
• Aggregate and clean data from GIS, asset management, and inspection systems to build repeatable analytical pipelines for risk scoring.
• Translate model outputs into prioritization tools and dashboards that support program strategy, resource allocation, and mitigation decisions.
• Validate model performance through monitoring, calibration, and sensitivity analysis while collaborating with subject matter experts.

Required Skills

• Bachelor's degree in Data Science, Statistics, or related field.
• 5+ years of experience in data science or quantitative analysis.
• Proficiency in Python and R for statistical modeling and machine learning.
• Strong SQL skills for data extraction and management.
• Experience with data wrangling and ETL processes to structure enterprise data.
• Ability to create visualizations and dashboards using Power BI or Tableau.
• Knowledge of geospatial or spatiotemporal modeling is advantageous.

Key Skills

Education

Bachelor's degree