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Senior
Senior Data Scientist, Fleet Performance Optimization
Confirmed live in the last 24 hours
Gridware
Compensation
$175k - $190k/per-year-salary
San Francisco, CA
On-site
Posted March 19, 2026
Job Description
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
As a Senior Data Scientist, you will be embedded within Gridware’s Fleet team, driving fleet performance optimization across our network of IoT devices.
You will work across hardware, firmware, connectivity, and backend systems to understand real-world system behavior and optimize performance end-to-end. A core focus of this role is balancing competing system constraints—such as power consumption, data fidelity, connectivity reliability, and anomaly detection latency—to ensure optimal fleet performance.
This role combines modeling, experimentation, and hands-on investigation to ensure reliable, scalable system performance in dynamic, real-world environments.
You will work across hardware, firmware, connectivity, and backend systems to understand real-world system behavior and optimize performance end-to-end. A core focus of this role is balancing competing system constraints—such as power consumption, data fidelity, connectivity reliability, and anomaly detection latency—to ensure optimal fleet performance.
This role combines modeling, experimentation, and hands-on investigation to ensure reliable, scalable system performance in dynamic, real-world environments.
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