Develop and operationalize machine learning and statistical models predicting weather-driven transmission and distribution outages. Integrate weather forecasts, infrastructure data, outage records, and geospatial layers into real-time forecasting pipelines. Apply spatio-temporal, probabilistic, and physics-based modeling to support utility decisions during extreme weather and wildfire events. Collaborate with meteorologists, risk modelers, software engineers, and utility customers while using agentic coding tools to accelerate development and validating scientific correctness. The summary above was generated by AI About Technosylva Technosylva is a global leader in wildfire and extreme weather risk mitigation software. The Company's market-leading solutions, enhanced by AI and machine learning capabilities, provide real-time and predictive insights to support electric utility, insurance and government agency customers. Technosylva has provided critical solutions for the past 26 years. In 2022 the organization entered a period of significant growth and transformation with investment from TA Associates, a leading growth PE firm, scaling to about 175 employees and offering its product in over 10 countries. In 2024 General Atlantic, a leading global growth investor, announced a strategic growth investment in Technosylva to support the company in its mission. Role Overview We are seeking a Senior Data Scientist with deep expertise in modeling the impact of extreme weather on electric grid infrastructure, with a particular focus on transmission outage prediction. In this role, you will design, build, and operationalize machine learning and statistical models that predict weather-driven outages and failures across transmission and distribution systems, directly supporting utility decision-making before and during extreme weather events. 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. 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. Requirements Education 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. Read Full Description Similar Jobs Eve Renewals Manager An Hour Ago Easy Apply Remote or Hybrid United States Easy Apply Junior Junior Legal Tech

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