Principal Data Scientist

Primary Job Title: Data Scientist, Principal

Location: Dublin, CA

Onsite Flexibility: Hybrid — 1–2 days per week onsite in Dublin at 5875 Arnold Dr Ste 200, Dublin; may require travel to other locations such as Oakland, Concord, or field sites around the service area

Contract Details:

• Position Type: Contract
• Pay Rate: $120.00–$130.00 / Hour (USD)
• Shift / Schedule: Monday–Friday
• Work Authorization: Applicants must be authorized to work for ANY employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.

Job 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 the 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 the 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, and 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, and 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, and 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 Experience:

• 5 years of experience in data science, predictive analytics, quantitative risk analysis, or statistical modeling.
• Experience working with large, complex, and imperfect datasets from multiple business systems.
• Demonstrated ability to turn ambiguous business problems into structured analytical approaches.

Nice-to-Have Experience:

• 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.
• 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.

Required Skills:

• Statistical inference and machine learning methodologies.
• Risk modeling and forecasting techniques.
• Feature engineering and data wrangling capabilities.
• Data quality management and assessment expertise.
• Building predictive models using Python, R, SQL, or similar tools.
• Ability to explain technical results to operational and executive audiences in a clear, concise, and decision-oriented manner.
• Programming in Python, R, and SQL.
• Statistical modeling, machine learning, forecasting, simulation, and optimization.
• Data wrangling, ETL concepts, and data quality assessment.
• Classification and probability prediction modeling.
• Risk scoring frameworks development.
• Time-to-event and hazard model construction.
• Explainable AI and interpretable model approaches.
• Scenario analysis and Monte Carlo methods.
• 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.

Preferred Skills:

• Visualization tools such as Power BI, Tableau, matplotlib, or seaborn.
• Geospatial tools including ArcGIS, QGIS, or GeoPandas.
• Spatial analysis techniques.
• Knowledge of safety and reliability risk concepts in utility operations.

Education Requirements:

• Bachelor's degree in Data Science, Statistics, Applied Mathematics, Engineering, Computer Science, Operations Research, Economics, or a related quantitative field.
• Master's or PhD in a quantitative discipline preferred.

Work Environment / Physical Requirements:

• Client laptop will be provided.
• PPE (hardhat, vest, safety glasses, etc.) will be provided if needed.
• With prior manager approval, may submit expenses at a set amount for internet/phone reimbursements.
• May require travel to other locations such as Oakland, Concord, or field sites around the service area.

Benefits:

• Medical, Vision, and Dental Insurance Plans
• 401k Retirement Fund

Important Notes:

• Local candidates only.

About The Company: Leading natural gas and electric energy company serving millions of customers across the United States. Offers reliable energy delivery and a positive work environment. Join our team and make a difference in your community.

About GTT: GTT is a minority-owned staffing firm and a subsidiary of Chenega Corporation, a Native American-owned company in Alaska. As a Native American-owned, economically disadvantaged corporation, we highly value diverse and inclusive workplaces. Our clients are Fortune 500 banking, insurance, financial services, and technology companies, along with some of the nation's largest life sciences, biotech, utility, and retail companies across the US and Canada. We look forward to helping you land your next great career opportunity!

Job Number: 26-05072