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Salary: USD 120,000 - 155,000 / annual
Verse is an energy intelligence platform for the AI economy, backed by Bessemer Venture Partners, GV, Coatue, and NVIDIA. The company solves critical infrastructure challenges by helping large energy consumers achieve faster, cheaper, and cleaner power through real-time control of energy assets and complete portfolio visibility.
As a Data Scientist on the Data Science Team, you will own quantitative research projects end-to-end, from problem formulation through model development to production integration. Your work directly influences customer decisions about energy procurement, asset dispatch, and hedging strategies. Example projects include backtesting financial performance of renewable energy projects, encoding energy contract terms into scalable models, and developing probabilistic models for medium-term energy price scenarios.
Key responsibilities include building and improving machine learning and statistical models for renewable generation forecasting, storage dispatch optimization, and energy price prediction. You will scope problems collaboratively with product, engineering, and customer teams; design solutions; validate results against industry benchmarks; and communicate findings to both technical and non-technical audiences. You'll integrate research outputs into Verse's cloud-based production environment, writing clean, well-documented Python code. You'll also develop domain expertise in electricity markets and renewable energy, understanding key drivers like load growth, market rule changes, and renewable deployment trends.
Minimum qualifications include a Master's degree in a quantitative field (engineering, computer science, economics, mathematics, operations research, physics, or similar) or a Bachelor's degree with 2+ years of energy sector experience. You need solid foundations in machine learning and statistical modeling, proficiency in Python (NumPy, pandas, scikit-learn), familiarity with wholesale electricity market fundamentals and renewable energy technologies, and the ability to communicate technical findings clearly.
Preferred qualifications include direct experience with US or European wholesale electricity markets, energy procurement, power purchase agreements, or risk management; experience modeling solar, wind, or energy storage systems; knowledge of mathematical optimization; experience shipping models to production; experience with probabilistic forecasting and uncertainty quantification; or a PhD in a relevant quantitative field.
