Senior Data Scientist, Forecasting

Location: San Francisco, CA (Hybrid)

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 Senior Data Scientist, Forecasting 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 Behind-The-Meter (BTM) battery storage. You will play a critical role in shaping our modeling and analytical data layer, leveraging machine learning techniques, supporting optimization modeling infrastructure, and developing scalable approaches to power Verse’s software offerings.

This position emphasizes strong technical depth in Python, machine learning, and analytical modeling, along with the ability to independently scope and execute complex projects end-to-end. Experience in electricity markets and energy systems is preferred.

Key Responsibilities

Lead End-to-End Data Science Projects: Own and drive large projects from problem definition through scoping, modeling, validation, and production deployment. Translate business problems into scalable, high-impact modeling solutions with minimal oversight.

Statistical Machine Learning Modeling: Design, develop, and refine statistical and machine learning models (e.g., time series forecasting, probabilistic models, optimization-linked models) to support decision-making and enhance product capabilities.

Analytics Engineering Data Modeling: Perform complex data transformations and develop well-structured analytical data models. Translate business and analytical requirements into scalable, tested, and well-documented datasets, with an emphasis on dimensional modeling and reproducibility (e.g., dbt-style workflows).

Software Development Productionization: Write clean, efficient, and maintainable Python code. Contribute to integrating models into production systems and model deployment pipelines in a cloud-based environment.

Exploratory Data Analysis Insight Generation: Apply statistical methods and data exploration techniques to uncover insights, validate assumptions, and inform modeling approaches.

Machine Learning and MLOps: Contribute to Verse’s machine learning modeling infrastructure to support scaling of ML models and improving reliability, monitoring, and performance in production.

Cross-Functional Collaboration: Partner with product, engineering, and business stakeholders to ensure models and insights are aligned with user needs and effectively integrated into workflows.

Technical Leadership: Mentor junior team members, contribute to best practices, and help shape the technical direction of modeling and analytics across the team.

What We're Looking For (Minimum Qualifications)

Master’s degree or higher in Computer Science, Statistics, Engineering, Applied Mathematics, or a related quantitative field. A bachelor’s degree with significant relevant experience may be considered.

5+ years of professional experience in data science, machine learning, or a related field

Proven track record of independently leading and delivering complex modeling or data science projects

Experience deploying and maintaining models in production environments

Strong Python expertise, including experience with scientific computing and ML libraries (e.g., NumPy, pandas, scikit-learn, PyTorch, TensorFlow)

Strong foundation in statistical modeling and machine learning, including time series forecasting and model evaluation

Hands-on experience in complex transformations, dimensional modeling, and translating analytical requirements into well-structured, tested, and documented models

What Will Make You Standout (Preferred Qualifications)

Experience in energy, climate tech, or related domains (not required)

Familiarity with optimization methods or operations research

Experience with real-time or streaming data systems

Prior experience mentoring or leading technical teams

PhD in a quantitative field

What Makes Verse a Great Place to Work?

Lead with Empathy: We lift each other up with humility and kindness, always putting colleagues and customers first

Be Honest Transparent: We prioritize effective communication to buil…