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Verse is an energy intelligence platform for the AI economy, backed by Bessemer Venture Partners, GV, Coatue, and NVIDIA. The company helps large energy consumers achieve faster, cheaper, and cleaner power by combining real-time control of energy assets with complete visibility into their energy portfolio.
As Senior Data Scientist, Infrastructure, you will lead the design, development, and deployment of advanced machine learning models powering Verse's software offerings for distributed battery storage, demand response, and renewable energy integration. You will be responsible for advancing cutting-edge forecasting algorithms that support the operation of these critical energy systems.
Key responsibilities include:
- Applying machine learning, data science, and statistical methods to advance forecasting capabilities embedded in software offerings
- Owning forecasting workflows that inform optimization methods for battery storage control, demand response, and electricity market participation
- Conducting advanced research on energy systems, including renewable energy (solar, wind), energy storage, and novel technologies like green hydrogen
- Using advanced data analysis, simulations, and energy modeling tools to forecast and provide insights on renewable and battery storage performance
- Collaborating across business units and with customers to translate research outcomes into real-world solutions
- Supporting decision-making under uncertainty through statistical analysis
Minimum qualifications: 5+ years deploying ML forecasting algorithms in production software; expertise in deep learning for time series forecasting and uncertainty quantification; 2+ years applying forecasting methods in energy industry; proficiency in Python and data analysis tools; deep understanding of energy technologies (solar, wind, battery storage, grid integration); familiarity with electricity markets.
Preferred: PhD in Engineering, Operations Research, Economics, or related field; knowledge of mathematical optimization and stochastic optimization; experience deploying ML/deep learning for battery operation forecasting; power markets forecasting background.
