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 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 build trust with our team, customers, and stakeholders
• Move with Balance & Precision: We believe speed and perseverance must be accompanied by thoughtfulness and reflection
• Leave the World a Better Place: We are passionate about our mission, and we strive to create a sustainable world for future generations

Base Pay Range

$190,000 - $230,000

This is the estimated base salary range for this position, which does not include the value of benefits or a potential equity grant. A wide range of factors are considered in making compensation decisions, including but not limited to skill sets, market conditions, experience and training, licensure and certifications, and business and organizational needs.

Benefits and Employee Perks

• Competitive compensation and equity grant at a high-growth start up
• Comprehensive benefits package including medical, dental and vision insurance, and 401k
• Flexible hours and unlimited PTO
• Diverse and inclusive working environment

Verse is an equal opportunity employer. All applicants and employees are considered for hire, promotion, and compensation without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, marital or familial status.