Data Scientist

SPECTRAFORCE; Dublin (CA); Hybrid; USD 220,416 - 227,304; Full time

Benefits offered by this job

Expense reimbursement for internet/phone

Client laptop provided

Personal protective equipment if needed

Job summary

SPECTRAFORCE is seeking a Data Scientist in Dublin to enhance their quantitative risk analysis and predictive analytics capability. The role supports the development of frameworks for transmission right of way risk reduction. Responsibilities include defining risk models, collaborating with teams, and implementing statistical techniques. The successful candidate should have at least 5 years of relevant experience and proficiency in Python, R, and SQL. This hybrid position offers $160-$165/hr with potential travel to various locations.

Qualifications

• 5+ years of experience in data science, predictive analytics, or statistical modeling.
• Ability to explain technical results to operational and executive audiences clearly.
• Required proficiency in programming with Python and R.

Responsibilities

• Develop quantitative risk frameworks for assessing encroachments.
• Create predictive models to foresee safety and reliability issues.
• Collaborate with cross-functional teams to translate data into actionable insights.

Skills

Statistical modeling

Machine learning

Predictive analytics

Data wrangling

Education

Bachelor’s degree in Data Science or related field

Tools

Python

R

SQL

Power BI

Tableau

ArcGIS

Job description

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

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

• 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.

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
• 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.

Apply statistical and machine learning techniques such as

• Logistic regression
• Survival analysis / time‑to‑event modeling
• Random forests / gradient boosting
• Scenario modeling and simulation
• 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
• 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.
• 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
• Evaluate tradeoffs across mitigation options
• Support resource allocation and investment decisions
• Support the development of business cases and analytical narratives for leadership, regulators, and governance forums.
• 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 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

• Statistical inference
• Machine learning
• Forecasting
• 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

• 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

• Classification and probability prediction
• Time-to-event / hazard models
• 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
• Ability to balance model sophistication with usability and explainability
• Excellent written and verbal communication skills
• For information on benefits, equal opportunity employment, and location‑specific applicant notices, click here

At SPECTRAFORCE, we are committed to maintaining a workplace that ensures fair compensation and wage transparency in adherence with all applicable state and local laws. This position's pay is: $160/hr - $165/Hour.