Data Scientist

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

• 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.

• 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.

• 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
• Build predictive models to estimate the likelihood of future safety or reliability events resulting from existing or emerging encroachments in transmission rights of way.

• Logistic regression
• Survival analysis / time‑to‑event modeling
• Random forests / gradient boosting
• Scenario modeling and simulation
• Geospatial and spatiotemporal modeling

• Proximity to energized assets
• Encroachment type and severity
• 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

• 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.
• Translate model outputs into practical prioritization tools that support program strategy, annual planning, and execution.

• Rank encroachments by risk
• Identify high‑priority mitigation opportunities
• Evaluate tradeoffs across mitigation options
• Support resource allocation and investment decisions
• Support the development of business cases and analytical…