Senior Data Scientist, Fleet Performance Optimization

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.

Responsibilities

Develop models and analyses to optimize system performance across competing constraints (e.g., power usage vs data quality vs responsiveness) Define and implement end-to-end observability , establishing metrics across system components and dependencies Design and run experiments (e.g., pre/post, control vs test) to evaluate changes and detect regressions at both component and system levels Build and refine anomaly detection and failure analysis methods across complex, real-world data Lead ad hoc investigations into system issues, identifying root causes and driving resolution with cross-functional teams Translate insights into actionable recommendations across Firmware, Hardware, Software, and Operations , driving measurable improvements in system behavior Develop predictive systems for early issue detection and performance forecasting , including in environments with limited historical data Continuously evolve analyses into scalable intelligence systems that support monitoring, decision-making, and automation

Required Skills 5+ years of experience in data science working on production systems or real-world applications Proven experience building, deploying, and maintaining models in production environments Strong proficiency in Python and SQL Experience working with complex, real-world datasets (e.g., time-series, event-based, or system-generated data) Strong foundation in statistical analysis, experimentation, and/or anomaly detection Proven ability to bring structure to ambiguous, open-ended problems , iterating quickly to drive toward practical, high-impact solutions (80/20 mindset) Experience working cross-functionally with engineering and operational teams

Bonus Skills Experience working on distributed hardware/software systems such as robotics, autonomous vehicles, IoT fleets, charging infrastructure, or energy/grid systems Prior ownership of end-to-end performance, reliability, or optimization for large-scale, real-world systems operating in dynamic environments