Description

Key Responsibilities:
Quantitative Risk Modeling
• Develop quantitative risk frameworks to assess the risk posed by encroachments within or adjacent to transmission rights of way.
• Define risk equations, scoring methodologies, and analytical models that estimate both:
o Likelihood of an event occurring (e.g., safety incident, reliability event, asset damage, access impairment, wildfire ignition, clearance violation, line contact, third-party interference), and
o Consequence / impact of that event.
• Incorporate multiple risk dimensions into a unified analytical framework, including:
o Public and employee safety
o Electric reliability / outage exposure
o Wildfire and ignition risk
o Regulatory and compliance exposure
o Asset damage and access limitations
o Financial and operational impact
Predictive Analytics & Machine Learning
• Build predictive models to estimate the likelihood of future safety or reliability events resulting from existing or emerging encroachments in transmission rights of way.
• Apply statistical and machine learning techniques such as:
o Logistic regression
o Survival analysis / time-to-event modeling
o Random forests / gradient boosting
o Bayesian methods
o Scenario modeling and simulation
o Geospatial and spatiotemporal modeling
• Identify leading indicators and risk drivers that increase the probability of an event, such as:
o Proximity to energized assets
o Encroachment type and severity
o Clearance deficits
o Structure condition / asset age
o Land use and development patterns
o Historical incident patterns
o Inspection findings
o Environmental and weather conditions
o Access constraints
o High Fire Threat District (HFTD) or other high-risk locations
Data Integration & Analytical Pipeline Development
• Aggregate, clean, and structure data from multiple enterprise and operational systems, including GIS, asset management, inspections, outage history, incident data, vegetation data, work management, and field observations.
• Develop repeatable analytical pipelines to support risk scoring, trend analysis, forecasting, and prioritization.
• Assess data quality, completeness, and lineage; identify data gaps and recommend improvements to enable stronger analytics.
• Partner with IT, data engineering, GIS, and business teams to improve data architecture and enable scalable model deployment.
Decision Support & Program Prioritization
• Translate model outputs into practical prioritization tools that support program strategy, annual planning, and execution.
• Develop dashboards, visualizations, and decision-support tools to help the business:
o Rank encroachments by risk
o Identify high-priority mitigation opportunities
o Forecast emerging risk hotspots
o Evaluate tradeoffs across mitigation options
o Support resource allocation and investment decisions
• Support the development of business cases and analytical narratives for leadership, regulators, and governance forums.
Monitoring, Validation & Continuous Improvement
• Establish model validation, calibration, and performance monitoring processes to ensure analytics remain accurate, explainable, and fit for purpose.
• Track model precision, recall, false positives/negatives, drift, and operational usefulness over time.
• Conduct sensitivity analyses, scenario testing, and back-testing against historical events.
• Continuously improve methodologies as new data sources, field intelligence, and business requirements emerge.

Cross-Functional Collaboration
• Partner closely with subject matter experts in transmission operations, inspection, engineering, wildfire mitigation, risk management, land/ROW, and compliance to ensure models reflect real-world operating conditions.
• Facilitate discussions to define risk taxonomy, modeling assumptions, thresholds, and action triggers.
• Communicate technical findings clearly to both technical and non-technical stakeholders, including senior leadership.

Required Qualifications:
• Bachelor's degree in Data Science, Statistics, Applied Mathematics, Engineering, Computer Science, Operations Research, Economics, or a related quantitative field.
• 5+ years of experience in data science, predictive analytics, quantitative risk analysis, or statistical modeling.
• Experience building predictive models using Python, R, SQL, or similar tools.
• Strong knowledge of:
o Statistical inference
o Machine learning
o Risk modeling
o Forecasting
o Feature engineering
o Data wrangling and data quality management
• Experience working with large, complex, and imperfect datasets from multiple business systems.
• Ability to explain technical results to operational and executive audiences in a clear, concise, and decision-oriented manner.
• Demonstrated ability to turn ambiguous business problems into structured analytical approaches.

Preferred Qualifications:
• Master's or PhD in a quantitative discipline.
• Experience in electric utility, transmission operations, wildfire risk, asset risk management, infrastructure risk, public safety risk, or reliability analytics.
• Experience with geospatial analytics, including GIS-based risk modeling.
• Familiarity with transmission asset data, ROW management, encroachment data, inspection data, outage/event history, or utility asset health data.
• Experience in regulated industries where transparency, traceability, and model explainability are essential.
• Knowledge of safety and reliability risk concepts in utility operations.
• Experience developing dashboards or decision-support tools using Power BI, Tableau, or similar platforms.
• Familiarity with cloud analytics environments and productionizing models for business use.

Technical Skills:
• Programming: Python, R, SQL
• Analytics: Statistical modeling, machine learning, forecasting, simulation, optimization
• Data tools: Data wrangling, ETL concepts, data quality assessment
• Visualization: Power BI, Tableau, matplotlib, seaborn, or similar
• Geospatial: ArcGIS, QGIS, GeoPandas, spatial analysis techniques
• Modeling concepts:
o Classification and probability prediction
o Risk scoring frameworks
o Time-to-event / hazard models
o Explainable AI / interpretable models
o Scenario analysis and Monte Carlo methods

Key Skills

Education

Bachelor's degree