SPECTRAFORCE
Data Scientist
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Principal Data Scientist in Dublin, CA (Hybrid 1-2 days/week) on a 12-month contract, offering $100-$160/hr. Key skills required include Python, R, SQL, and experience in predictive analytics and risk modeling. Security clearance is necessary.
🌎 - Country
United States
💱 - Currency
$ USD
-
💰 - Day rate
160
-
🗓️ - Date
May 9, 2026
🕒 - Duration
Unknown
-
🏝️ - Location
Hybrid
-
📄 - Contract
Unknown
-
🔒 - Security
Yes
-
📍 - Location detailed
Dublin, CA
-
🧠 - Skills detailed
#Pandas #Tableau #SQL (Structured Query Language) #Microsoft Power BI #Monitoring #ML (Machine Learning) #Regression #Visualization #Datasets #Python #Trend Analysis #Data Science #Scala #BI (Business Intelligence) #Logistic Regression #Compliance #Forecasting #Cloud #Model Validation #R
Role description
Principal Data Scientist
Dublin, CA (Hybrid 1-2 days in a week)
12 Contract
Pay Range: $100-$160/hr
Develop and operationalize quantitative risk models to assess transmission ROW encroachment risks related to safety, reliability, wildfire, compliance, and asset integrity.
Build predictive analytics and machine learning models (logistic regression, survival analysis, random forests, Bayesian models, geospatial analytics) to forecast potential safety and reliability events.
Analyze and integrate large-scale enterprise datasets from GIS, inspections, outage history, asset management, vegetation management, and field operations systems.
Design repeatable analytical pipelines for risk scoring, forecasting, prioritization, trend analysis, and scenario simulation.
Identify leading risk indicators such as clearance violations, proximity to energized assets, asset age, environmental conditions, wildfire exposure, and historical incident patterns.
Develop dashboards, visualizations, and decision-support tools using Power BI, Tableau, Python, or similar technologies to support operational and executive decision-making.
Perform model validation, calibration, back-testing, sensitivity analysis, and ongoing performance monitoring to ensure explainability, accuracy, and regulatory transparency.
Collaborate cross-functionally with engineering, transmission operations, GIS, wildfire mitigation, compliance, inspection, and asset management teams to align analytical solutions with business objectives.
Translate ambiguous operational problems into structured analytical frameworks and communicate technical insights effectively to both technical and non-technical stakeholders.
Utilize Python, R, SQL, geospatial tools (ArcGIS/QGIS/GeoPandas), and cloud-based analytics environments to deliver scalable, production-ready risk analytics solutions.
Principal Data Scientist
Dublin, CA (Hybrid 1-2 days in a week)
12 Contract
Pay Range: $100-$160/hr
Develop and operationalize quantitative risk models to assess transmission ROW encroachment risks related to safety, reliability, wildfire, compliance, and asset integrity.
Build predictive analytics and machine learning models (logistic regression, survival analysis, random forests, Bayesian models, geospatial analytics) to forecast potential safety and reliability events.
Analyze and integrate large-scale enterprise datasets from GIS, inspections, outage history, asset management, vegetation management, and field operations systems.
Design repeatable analytical pipelines for risk scoring, forecasting, prioritization, trend analysis, and scenario simulation.
Identify leading risk indicators such as clearance violations, proximity to energized assets, asset age, environmental conditions, wildfire exposure, and historical incident patterns.
Develop dashboards, visualizations, and decision-support tools using Power BI, Tableau, Python, or similar technologies to support operational and executive decision-making.
Perform model validation, calibration, back-testing, sensitivity analysis, and ongoing performance monitoring to ensure explainability, accuracy, and regulatory transparency.
Collaborate cross-functionally with engineering, transmission operations, GIS, wildfire mitigation, compliance, inspection, and asset management teams to align analytical solutions with business objectives.
Translate ambiguous operational problems into structured analytical frameworks and communicate technical insights effectively to both technical and non-technical stakeholders.
Utilize Python, R, SQL, geospatial tools (ArcGIS/QGIS/GeoPandas), and cloud-based analytics environments to deliver scalable, production-ready risk analytics solutions.
