Quantitative Analyst
Verse Medical - San Francisco, CA
Posted Jun 10, 2026
Benefits
- Parental leave
- Not verified
- Non-birth-parent leave
- Not verified
- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
- Not verified
- Relocation assistance
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- Childcare support
- Not verified
- Learning budget
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- Verification
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- Salary
- $120K-$155K not verified - source not recorded; timestamp not recorded
- 401(k) match
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Schedule
- Shift type
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- Weekend work
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Application
- Cover letter
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- Assessment
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- Deadline
- Not stated
Where they hire
State eligibility is not yet verified.
About this role
Quantitative Analyst San Francisco, CA Location: San Francisco (Hybrid in SF highly preferred, US only) What is Verse? Energy markets are more volatile than ever. Rapid electrification and the rise of AI are driving unprecedented demand for power, while energy costs continue to rise across the globe. For the world's largest energy buyers, managing energy has never been more complex or more critical. Verse helps these organizations manage complex power portfolios with confidence by unifying energy data, planning, forecasting, and operations in one tool. Our Energy Cost Intelligence platform, Aria , brings together energy, finance, and operations teams with real-time, finance-ready intelligence-replacing spreadsheets and consultants with precision across the entire energy lifecycle. Built by an expert team of energy buyers, data scientists, and engineers, Verse enables faster, smarter energy decisions that reduce risk and lower energy costs. The Role Verse is seeking a Quantitative Analyst to join our Data Science Team. In this position, you will own research projects end-to-end from problem formulation through model development to production integration. Your work feeds directly into Aria and into decisions our customers make about how to procure, dispatch, and hedge their energy assets. For example, you might spend a cycle backtesting financial performance of renewable energy projects, encoding bespoke energy contract terms into reusable and scaled models, or developing a probabilistic model for medium-term energy price scenarios. Key Responsibilities Model & Forecast: Build and improve models for renewable generation, storage dispatch, and energy price prediction using machine learning, statistical methods, and optimization
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