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Über
Dublin, CA (Hybrid
1-2 days in a week)
12 Contract
**LOCAL CANDIDATES ONLY**
The role is Hybrid. 1-2 days a week in Dublin. There may be times when we need to travel to other locations such as Oakland, Concord, or field sites around the service area.
**Client laptop will be provided**PPE: Client will provide, if needed, hardhat, vest, safety glasses, etc.
**With prior
Manager approval, may submit expense, at a set amount for internet/phone reimbursements
Position Summary:
We are seeking a highly analytical and mission-driven Data Scientist to support the development of a quantitative risk analysis and predictive analytics capability for Transmission Right of Way (ROW) Risk Reduction Strategy.
This role will help design and operationalize data-driven methods to quantify risk, prioritize encroachments, and predict the likelihood of safety and reliability events associated with transmission right of way encroachments.
The successful candidate will partner with cross-functional teams across electric operations, asset management, vegetation management, engineering, risk, compliance, GIS, inspection, and program management to translate field, asset, and operational data into actionable insights.
The Data Scientist will build models that enable proactive decision-making by identifying where encroachments pose the greatest potential threat to public safety, worker safety, grid reliability, asset integrity, and wildfire risk.
This role is ideal for someone who combines deep technical expertise in statistical modeling and machine learning with the ability to work in complex operational environments and communicate insights to business and executive stakeholders.
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: 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 Consequence / impact of that event.
Incorporate multiple risk dimensions into a unified analytical framework, including:
Public and employee safety
Electric reliability / outage exposure
Wildfire and ignition risk
Regulatory and compliance exposure
Asset damage and access limitations
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:
Logistic regression
Survival analysis / time-to-event modeling
Random forests / gradient boosting
Bayesian methods
Scenario modeling and simulation
Geospatial and spatiotemporal modeling
Identify leading indicators and risk drivers that increase the probability of an event, such as:
Proximity to energized assets
Encroachment type and severity
Clearance deficits
Structure condition / asset age
Land use and development patterns
Historical incident patterns
Inspection findings
Environmental and weather conditions
Access constraints
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:
Rank encroachments by risk
Identify high-priority mitigation opportunities
Forecast emerging risk hotspots
Evaluate tradeoffs across mitigation options
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:
Statistical inference
Machine learning
Risk modeling
Forecasting
Feature engineering
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:
Classification and probability prediction
Risk scoring frameworks
Time-to-event / hazard models
Explainable AI / interpretable models
Scenario analysis and Monte Carlo methods
Key Competencies
Strong problem-solving and structured thinking
Ability to work across technical and operational disciplines
High attention to detail and analytical rigor
Strong business acumen and decision orientation
Comfort working in evolving, ambiguous problem spaces
Ability to balance model sophistication with usability and explainability
Excellent written and verbal communication skills
Sprachkenntnisse
- English
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