- Develop machine learning models that detect underperformance, faults, and anomalies in PV and storage system time-series data.
- Build diagnostic logic to identify root causes such as soiling, shading, inverter clipping, string outages, and sensor drift.
- Own the data science lifecycle, including problem framing, prototyping, model development, validation, deployment, and monitoring.
- Partner with Engineering to integrate models into the Resolv platform and translate outputs into prioritized dispatch recommendations.
- Close the feedback loop with operations to evaluate model performance in the field and iterate on designs.
- Shape the technical direction for the detection and diagnostics roadmap.
- Explore and prototype new modeling approaches to validate business impact.
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