Senior Data Engineer-Right of Way(ROW)Technology Risk and Reduction

Requisition ID # 174879 Job Category: Accounting / Finance Job Level: Individual Contributor Business Unit: Energy Delivery Work Type: Hybrid Job Location: Oakland; Alameda; Alta; American Canyon; Angels Camp; Antioch; Auberry; Auburn; Avenal; Avila Beach; Bakersfield; Balch Camp; Bay Point; Bear Valley; Belden; Bellota; Belmont; Benicia; Berkeley; Brentwood; Brisbane; Buellton; Burney; Buttonwillow; Calistoga; Campbell; Canyon Dam; Canyondam; Capitola; Caruthers; Chico; Clearlake; Clovis; Coalinga; Colusa; Concord; Concord; Corcoran; Cotati; Cottonwood; Cupertino; Daly City; Danville; Davis; Dinuba; Downieville; Dublin; Emeryville; Eureka; Fairfield; Folsom; Fort Bragg; Fortuna; Fremont; French Camp; Fresno; Fresno; Fulton; Garberville; Geyserville; Gilroy; Goodyear; Grass Valley; Guerneville; Half Moon Bay; Hayward; Hinkley; Hollister; Holt; Huron; Jackson; Kerman; King City; Lakeport; Lemoore; Lincoln; Linden; Livermore; Lodi; Loomis; Los Banos; Lower Lake; Madera; Magalia; Manteca; Manton; Mariposa; Martell; Marysville; Maxwell; Menlo Park; Merced; Meridian; Millbrae; Milpitas; Modesto; Monterey; Montgomery Creek; Morgan Hill; Morro Bay; Moss Landing; Mountain View; Napa; Needles; Newark; Newman; Novato; Oakdale; Oakhurst; Oakley; Olema; Orinda; Orland; Oroville; Palo Alto; Palo Cedro; Paradise; Parkwood; Paso Robles; Petaluma; Pioneer; Pismo Beach; Pittsburg; Placerville; Pleasant Hill; Pleasanton; Point Arena; Potter Valley; Quincy; Rancho Cordova; Red Bluff; Redding; Richmond; Ridgecrest; Rio Vista; Rocklin; Roseville; Round Mountain; Sacramento; Salida; Salinas; San Bruno; San Carlos; San Francisco; San Francisco; San Jose; San Luis Obispo; San Mateo; San Rafael; San Ramon; San Ramon; Sanger; Santa Cruz; Santa Maria; Santa Nella; Santa Rosa; Selma; Shaver Lake; Sonoma; Sonora; South San Francisco; Springville; Stockton; Storrie; Taft; Tracy; Turlock; Twain; Ukiah; Vacaville; Vallejo; Walnut Creek; Wasco; Watsonville; West Sacramento; Wheatland; Whitmore; Willits; Willow Creek; Willows; Windsor; Winters; Woodland; Yuba City 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, andpredict 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. This position is hybrid, working from your remote office and your assigned location at least two days a week and or based on business need. PG&E is providing the salary range that the company in good faith believes it might pay for this position at the time of the job posting. This compensation range is specific to the locality of the job. The actual salary paid to an individual will be based on multiple factors, including, but not limited to, specific skills, education, licenses or certifications, experience, market value, geographic location, and internal equity. Although we estimate the successful candidate hired into this role will be placed towards the middle or entry point of the range, the decision will be made on a case-by-case basis related to these factors. Bay Area Minimum: $140,000 Bay Area Mid:$189,000 Bay Area Maximum: $238,000 California Minimum: $133,000 California Mid: $180,000 California Maximum:$226,000 This job is also eligible to participate in PG&E’s discretionary incentive compensation programs. Job 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), andConsequence / impact of that event.Incorporate multiple risk dimensions into a unified analytical framework, including:Public and employee safetyElectric reliability / outage exposureWildfire and ignition riskRegulatory and compliance exposureAsset damage and access limitationsFinancial 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 regressionSurvival analysis / time-to-event modelingRandom forests / gradient boostingBayesian methodsScenario modeling and simulationGeospatial and spatiotemporal modelingIdentify leading indicators and risk drivers that increase the probability of an event, such as:Proximity to energized assetsEncroachment type and severityClearance deficitsStructure condition / asset ageLand use and development patternsHistorical incident patternsInspection findingsEnvironmental and weather conditionsAccess constraintsHigh 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 riskIdentify high-priority mitigation opportunitiesForecast emerging risk hotspotsEvaluate tradeoffs across mitigation optionsSupport resource allocation and investment decisionsSupport 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. Minimum Qualifications Bachelor’s degree in Data Science, Statistics, Applied Mathematics, Engineering, Computer Science, Operations Research, Economics, or a related quantitative field.6 of experience in data science, predictive analytics, quantitative risk analysis, or statistical modeling. Desired Master’s or PhD in a quantitative discipline.Experience building predictive models using Python, R, SQL, or similar tools.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.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 useStrong problem-solving and structured thinkingAbility to work across technical and operational disciplinesHigh attention to detail and analytical rigorStrong business acumen and decision orientationComfort working in evolving, ambiguous problem spacesAbility to balance model sophistication with usability and explainabilityExcellent written and verbal communication skillsProgramming:Python, R, SQLAnalytics: Statistical modeling, machine learning, forecasting, simulation, optimizationData tools: Data wrangling, ETL concepts, data quality assessmentVisualization: Power BI, Tableau, matplotlib, seaborn, or similarGeospatial: ArcGIS, QGIS, GeoPandas, spatial analysis techniquesStrong knowledge of statistical inference,machine learning,risk modeling,forecasting, feature engineering, data wranging and data quality managementModeling concepts:Classification and probability predictionRisk scoring frameworksTime-to-event / hazard modelsExplainable AI / interpretable modelsScenario analysis and Monte Carlo methods