Data Scientist - Transmission ROW Risk Analytics

We are looking for a Data Scientist Transmission ROW Risk Analytics for our client in Dublin ,

CA Job Title:

Data Scientist Transmission ROW Risk Analytics Job Location:

Dublin ,

CA Job Type:

Contract Job Overview:

Pay Range:

$158.50hr - $163.50hr

Requirement/Must Have:

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, risk modeling, forecasting, feature engineering, and data quality management. Experience working with large, complex, and imperfect datasets from multiple business systems. Strong analytical, problem-solving, and communication skills. Ability to explain technical findings to operational and executive stakeholders. Experience translating business problems into structured analytical solutions.

Experience:

Experience with predictive analytics and machine learning techniques such as logistic regression, survival analysis, random forests, gradient boosting, Bayesian methods, and scenario modeling. Experience with geospatial and spatiotemporal modeling techniques. Experience developing dashboards and decision-support tools. Experience collaborating with cross-functional operational and technical teams. Experience with model validation, calibration, sensitivity analysis, and performance monitoring.

Responsibilities:

Develop quantitative risk frameworks to assess risks associated with transmission ROW encroachments. Build analytical models to estimate the likelihood and impact of safety, reliability, wildfire, compliance, and operational events. Develop predictive models to identify leading indicators and risk drivers. Aggregate, clean, and structure data from GIS, inspections, outage history, incident data, vegetation data, and asset management systems. Develop repeatable analytical pipelines for forecasting, trend analysis, and prioritization. Create dashboards, visualizations, and prioritization tools to support planning and resource allocation. Conduct scenario testing, back-testing, and model performance evaluations. Partner with business, engineering, GIS, and IT teams to improve data architecture and model deployment. Communicate analytical findings and recommendations to technical and non-technical stakeholders. Support business cases and analytical reporting for leadership and governance forums.

Should H ave:

Master s or PhD in a quantitative discipline. Experience in electric utility, transmission operations, wildfire risk, asset risk management, or reliability analytics. Experience with GIS-based risk modeling and geospatial analytics. Familiarity with transmission asset data, inspection data, outage history, and utility asset health data. Experience in regulated industries requiring transparency and explainable models. Familiarity with cloud analytics environments and productionized machine learning models.

Skills:

Python. R. SQL. Statistical modeling. Machine learning. Forecasting. Simulation and optimization. Data wrangling and ETL concepts. Data quality assessment. Power BI. Tableau. Arc

GIS. QGIS.

GeoPandas. Spatial analysis techniques. Classification and probability prediction. Risk scoring frameworks. Time-to-event and hazard models. Explainable AI and interpretable models. Scenario analysis and Monte Carlo methods.