Senior Data Scientist, Outage & Extreme Weather

Technosylva

. Design, develop, and validate machine learning models to predict transmission outages driven by extreme weather .

Posted 9/23/2026full-timeRemote • SpainSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in developing and operationalizing machine learning models for predicting transmission outages due to extreme weather, with a strong foundation in statistical modeling, geospatial analysis, and collaboration with cross-functional teams. Proficient in utilizing advanced coding tools and optimizing workflows for real-time forecasting applications.

Highest-signal resume keywords

Machine Learning Model DevelopmentStatistical Modeling for Grid ReliabilityGeospatial Data AnalysisPython Programming (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch)Collaboration with Meteorologists and Risk Modelers

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningStatistical ModelingTransmission Outage PredictionSpatio-Temporal ModelingEnsemble MethodsNeural NetworksProbabilistic ModelsPhysics-Based ModelingGeospatial AnalysisReal-Time Forecasting

Soft Skills

Clear CommunicationProblem DecompositionJudgment in Code Review

Tools & Technologies

GeoPandasArcGISClaude CodeCursorCopilotRSQLJulia

Industry Keywords

Environmental EngineeringAtmospheric ScienceCivil EngineeringData ScienceEnergy SectorUtilitiesISOs/RTOsWildfire RiskExtreme Weather Impacts

Tech Stack

Tools & technologies

NumpyPandasPythonPyTorchRTOSScikit-LearnSQLTensorflow

About the role

Key responsibilities & impact

Design, develop, and validate machine learning models to predict transmission outages driven by extreme weather
Build spatio-temporal models linking weather forecasts to infrastructure failure risk, including probability of failure estimates for transmission and distribution assets
Develop models characterizing the relationship between transmission outages, extreme weather events, and wildfire ignition risk
Integrate weather model output, asset and infrastructure data, historical outage records, and geospatial layers into robust, reproducible modeling pipelines
Operationalize research-grade models into fast, reliable production systems for real-time forecasting workflows
Evaluate and benchmark model performance against state-of-the-art methods
Communicate accuracy, skill, and uncertainty to internal teams and utility customers
Collaborate with meteorologists, risk modelers, and software engineers to improve Technosylva’s outage and extreme weather products
Leverage agentic coding tools to accelerate model prototyping, pipeline development, testing, and documentation while maintaining rigorous review and validation standards

Requirements

What you’ll need

Ph.D. in Environmental Engineering, Atmospheric Science, Civil Engineering, Statistics, Data Science, or a related quantitative field strongly preferred
A master’s degree with substantial applied experience in weather-driven outage or infrastructure risk modeling will be considered
Demonstrated experience developing transmission outage prediction models
5+ years of experience (academic or industry) applying statistical modeling and machine learning to grid reliability, storm outage prediction, or related energy-sector problems
Experience working with utilities, ISOs/RTOs, or grid operators on weather-related operational forecasting is highly valued
Track record of peer-reviewed publications, patents, or deployed production models in outage prediction, wildfire risk, or extreme weather impacts
Strong grounding in ensemble methods, neural networks, probabilistic models, and statistical modeling for spatio-temporal problems
Experience combining physics-based/mechanistic models with data-driven approaches for infrastructure failure prediction
Proficiency with geospatial data and tools, including GeoPandas, ArcGIS or equivalent, and large multidimensional weather datasets
Advanced Python skills, including NumPy, Pandas, Scikit-learn, TensorFlow or PyTorch; experience with R, SQL, or Julia is a plus
Ability to optimize model runtime and computational workflows for real-time operational use
Hands-on experience using agentic coding tools such as Claude Code, Cursor, Copilot agents, or similar as a core part of daily development workflows
Skilled at writing clear specifications, decomposing problems, and providing context for AI agents
Strong judgment in reviewing and validating agent-generated code, especially for scientific correctness in modeling pipelines

Benefits

Comp & perks

Competitive annual salary
Private health insurance
Flexible benefits plan, allowing you to tailor part of your compensation package to your personal needs
Annual bonus based on individual performance and company results
Flexible working hours
Remote work options