Senior Data Scientist, Fleet Performance Optimization

Gridware is a technology company focused on 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 mechanical 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 headquartered in San Francisco, California, and is backed by climate-tech and Silicon Valley investors. For more information, please visit www.Gridware.io.

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.

Health, Dental & Vision (Gold and Platinum with some providers plans fully covered) 

Paid parental leave 

Alternating day off (every other Monday)

“Off the Grid”, a two week per year paid break for all employees. 

Commuter allowance 

Company-paid training 

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

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

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