Senior Data Scientist, Outage & Extreme Weather
New
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TechnosylvaEnergy Data Science
Spain OnlyFull-TimeSenior
SalaryCompetitive annual salary.
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonMachine LearningPyTorchTensorflowscikit-learn
Requirements
- Ph.D. in Environmental Engineering, Atmospheric Science, Civil Engineering, Statistics, Data Science, or a related quantitative field strongly preferred.
- 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.
- Strong grounding in machine learning methods (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 (GeoPandas, ArcGIS or equivalent) and large multidimensional weather datasets.
- Advanced Python skills (NumPy, Pandas, Scikit-learn, TensorFlow or PyTorch) with the ability to write clean, well-documented, production-quality code.
- Hands-on experience using agentic coding tools (Claude Code, Cursor, Copilot agents, or similar) as a core part of daily development workflows.
- Skilled at structuring work for AI agents: writing clear specifications, decomposing problems, and providing context so agents produce correct, maintainable code.
Responsibilities
- Design, develop, and validate machine learning models to predict transmission outages driven by extreme weather, combining mechanistic and probabilistic approaches.
- Build spatio-temporal models that link weather forecasts to infrastructure failure risk, including probability of failure (POF) estimates for transmission and distribution assets.
- Develop models characterizing the interrelationship between transmission outages, extreme weather events, and wildfire ignition risk.
- Integrate heterogeneous datasets—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 suitable for real-time forecasting workflows.
- Evaluate and benchmark model performance against state-of-the-art methods and clearly 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 product capabilities.
- Leverage agentic coding tools throughout the development lifecycle—using AI agents to accelerate model prototyping, pipeline development, testing, and documentation—while maintaining rigorous review and validation standards.
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