Principal Data Scientist

Title: Data Scientist, Principal Location: Dublin, CA Duration: 12 months Description: • • LOCAL CANDIDATES ONLY • • The role is Hybrid. 1-2 days a week in Dublin. Job Description: 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 Predictive Analytics & Machine Learning Data Integration & Analytical Pipeline Development Decision Support & Program Prioritization Monitoring, Validation & Continuous Improvement Cross-Functional Collaboration 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 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