> Markdown version of [/jobs/ext/3043810-data-scientist-outage-extreme-weather](https://www.wearedevelopers.com/jobs/ext/3043810-data-scientist-outage-extreme-weather). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist, Outage & Extreme Weather - **Company:** Technosylva - **Location:** Spain (Remote available) - **Contract:** Permanent contract - **Skills:** Geographic Information Systems, Artificial Intelligence, ArcGIS (Software), Artificial Neural Networks, Cursor (Graphical User Interface Elements), Python (Programming Language), Machine Learning, NumPy, Real-Time Operating Systems, Tensorflow, SQL Databases, Pytorch, Pandas, Scikit Learn, Data Analytics, Data Pipelines - **Published:** September 24, 2026 - **Apply:** https://startup.jobs/senior-data-scientist-outage-extreme-weather-spain-only-technosylva-10175279 ## About the Role * 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. Professional Experience * Demonstrated experience developing transmission outage prediction models-this is a core requirement for the role. * 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. Modeling & Technical Skills * 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; experience with R, SQL, or Julia is a plus. * Ability to optimize model runtime and computational workflows for real-time operational use. Agentic Coding & AI-Assisted Development * Hands-on experience using agentic coding tools (Claude Code, Cursor, Copilot agents, or similar) as a core part of daily development workflows-not just autocomplete, but delegating multi-step coding tasks to AI agents. * Skilled at structuring work for AI agents: writing clear specifications, decomposing problems, and providing context so agents produce correct, maintainable code. * Strong judgment in reviewing and validating agent-generated code, especially for scientific correctness in modeling pipelines. ## Description You will work at the intersection of atmospheric science, power systems, and machine learning-combining mechanistic, physics-based understanding of infrastructure failure with data-driven probabilistic methods. Your models will feed real-time operational products used by utilities to anticipate outages, position crews, and manage grid risk during storms, extreme winds, and wildfire conditions. RESPONSABILITIES * 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. ## Related Videos - [Advanced Typing in TypeScript](https://www.wearedevelopers.com/videos/496-advanced-typing-in-typescript) - [Vectorize all the things! 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