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.
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.
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.
BENEFITS
- Competitive annual salary.
- Private health insurance and a 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.
At Technosylva, we value diverse experiences and skills, and we understand that every career path is unique. We encourage anyone who believes they meet most of the requirements and is interested in growing and contributing in this role to apply.
DISCLAIMER
Final salary and benefits will depend on a variety of factors, including location, experience, training, qualifications, and market conditions.
EQUAL OPPORTUNITY STATEMENT
Technosylva is an Equal Opportunity Employer. We are committed to fostering an inclusive environment where diverse perspectives lead to better solutions.
