Senior Data Scientist, Outage & Extreme Weather
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Job Description
Senior Data Scientist modeling extreme-weather transmission outages and wildfire risk. Building real-time machine-learning products for Technosylva’s utility and government customers.
Responsibilities:
- Design, develop, and validate machine learning models predicting 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 relationships among 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 outage and extreme weather products
- Use AI agents to accelerate model prototyping, pipeline development, testing, and documentation while maintaining rigorous review and validation standards
Requirements:
- Demonstrated experience developing transmission outage prediction models (core requirement)
- 5+ years of academic or industry experience 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 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
- Experience with large multidimensional weather datasets
- Advanced Python skills, including NumPy, Pandas, Scikit-learn, TensorFlow or PyTorch
- Ability to write clean, well-documented, production-quality code
- 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 structuring work for AI agents through clear specifications, problem decomposition, and contextual guidance
- Strong judgment reviewing and validating agent-generated code for scientific correctness
- Ph.D. in Environmental Engineering, Atmospheric Science, Civil Engineering, Statistics, Data Science, or related quantitative field strongly preferred
- Master's degree with substantial applied experience in weather-driven outage or infrastructure risk modeling considered
- Experience with R, SQL, or Julia is a plus
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