Senior Data Scientist, Fleet Health

About Gridware Gridware is a San Francisco-based technology company dedicated to protecting and enhancing the electrical grid. We pioneered a groundbreaking new class of grid management called active grid response (AGR), focused on monitoring the electrical, physical, and environmental aspects of the grid that affect reliability and safety. Gridware’s advanced Active Grid Response platform uses high-precision sensors to detect potential issues early, enabling proactive maintenance and fault mitigation. This comprehensive approach helps improve safety, reduce outages, and ensure the grid operates efficiently. The company is backed by climate-tech and Silicon Valley investors. For more information, please visit www.Gridware.io.

Role Description The Data Scientist, Fleet Health will be a key member of Gridware’s Fleet team, focused on building fleet health predictive models, thresholds, and optimization frameworks to ensure 99% uptime across our rapidly scaling network of IoT devices. This role will drive the development of advanced intelligence for forecasting device health, predicting potential installation performance, and establishing data-driven criteria for detecting and remediating device degradation.

The ideal candidate will bring a strong foundation in applied machine learning and forecasting, comfort working with real-world sensor and fleet data, and a passion for scaling intelligent systems that power resilient clean energy infrastructure. You will work closely with engineering, operations, and product teams to apply insights that both optimize today’s fleet performance and inform the evolution of tomorrow’s solutions.

Develop predictive and forecasting models to anticipate device health issues, solar availability, and connectivity performance, driving proactive interventions and deployment planning. Define, test, and refine health thresholds to classify degradations, detect regressions, and optimize fleet performance across diverse operating conditions. Design and evaluate statistical tests and simulations to measure impact, uncover downtime drivers, and identify optimization opportunities. Collaborate with engineering, fleet operations, and software teams to embed intelligence into automated monitoring and remediation pipelines at scale. Communicate insights and contribute to scalable fleet intelligence frameworks that support growth from tens of thousands to millions of devices.

Bachelor’s, Master’s degree, or PhD in Engineering, Statistics, Data Science, or a related quantitative field. 5+ years of applied experience in data science with large-scale, real-world systems (IoT, clean tech, connectivity, or related domains). Expertise in forecasting, anomaly detection, and predictive modeling, with experience handling time-series, sensor, or geospatial datasets. Proficiency in Python with scientific and ML libraries (NumPy, Pandas, SciPy, scikit-learn, Keras, PyTorch) and familiarity with data platforms (SQL, Spark, etc.). Strong communicator with the ability to design experiments, validate models, and translate technical findings into actionable insights for cross-functional teams.

Experience forecasting solar generation, connectivity performance, or energy resource availability. Exposure to distributed sensing systems in energy, seismic monitoring, aerospace, industrial IoT, or environmental science. Background in working with power-constrained, communication-constrained, or solar-powered devices. Experience scaling predictive systems across large and growing hardware fleets.

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