Data Scientist

<p><strong>Location: </strong>San Francisco, CA (Hybrid)&nbsp;</p> <p><strong>What is Verse?&nbsp;</strong></p> <p>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.</p> <p>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.</p> <p><strong>The Role</strong></p> <p>Verse is seeking a Data Scientist to join our Data Science Team. In this role, you will lead the development and deployment of advanced data-driven solutions across a range of applications, including electricity markets, renewable procurement, and energy risk management. You will shape the machine learning and data modeling foundations that Verse's software is built on. For example, you might spend a cycle deploying electricity market price forecasting pipelines for new regions, developing solar production anomaly detection models, or creating scalable tools for benchmarking energy project financial performance.</p> <p><strong>Key Responsibilities</strong></p> <ul> <li><strong>Lead End-to-End Data Science Projects: </strong>Own and drive projects from problem definition through scoping, modeling, validation, and production deployment. Translate business problems into scalable, high-impact modeling solutions.</li> <li><strong>Statistical &amp; Machine Learning Modeling: </strong>Design, develop, and refine statistical and machine learning models (e.g., time series forecasting, probabilistic models) to support decision-making and enhance product capabilities.</li> <li><strong>Analytics Engineering &amp; Data Modeling: </strong>Perform complex data transformations and develop well-structured data models. Translate business and analytical requirements into scalable, tested, and well-documented datasets.</li> <li><strong>Software Development &amp; Productionization: </strong>Write clean, efficient, and maintainable Python code. Contribute to integrating models into production systems in a cloud-based environment while leveraging AI coding tools to accelerate development.</li> <li><strong>MLOps: </strong>