Job Title: Data Scientist Transmission ROW Risk Analytics
Location: Dublin, CA
Duration: May 20, 2026 May 19 2027
Pay Rate: $160.00/hr. W2
**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.
Job Overview
We are seeking a highly motivated and technically skilled Data Scientist specializing in Transmission Right-of-Way (ROW) Risk Analytics to join our dynamic team. In this role, you will leverage advanced data analytics, machine learning, and AI techniques to assess and mitigate risks associated with transmission corridors. Your expertise will drive innovative solutions that enhance the safety, reliability, and efficiency of our transmission infrastructure. You will work at the intersection of big data, natural language processing, and model deployment to deliver actionable insights that support strategic decision-making across the organization.
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.
Responsibilities
- Develop and implement machine learning models using frameworks such as TensorFlow and Spark to analyze transmission ROW risk factors.
- Conduct data mining and natural language processing (NLP) to extract meaningful insights from unstructured data sources like reports, surveys, and geospatial information.
- Design and optimize ETL processes to integrate data from diverse sources including Hadoop, SQL databases, and linked data repositories for comprehensive risk assessment.
- Collaborate with cross-functional teams to design scalable database schemas and ensure efficient data storage using best practices in database design.
- Utilize cloud platforms such as AWS for model training, deployment, and management of AI solutions in a secure environment.
- Apply statistical techniques with SAS, R, or Python to perform predictive analytics and identify risk patterns within transmission corridors.
- Develop dashboards and visualizations using Looker or similar tools to communicate complex findings clearly to stakeholders.
- Support model deployment efforts by integrating machine learning models into operational systems using Java, Bash scripts, or VBA automation tools.
- Stay current with emerging technologies like quantum engineering and big data analytics to continuously enhance risk assessment methodologies.
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 inferenceo Machine learningo Risk modelingo Forecastingo Feature engineeringo 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 predictiono Risk scoring frameworkso Time-to-event / hazard modelso Explainable AI / interpretable modelso 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
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