Location: San Francisco, CA (Hybrid)
The RoleVerse 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.
Key Responsibilities- Lead End-to-End Data Science Projects: Own and drive projects from problem definition through scoping, modeling, validation, and production deployment. Translate business problems into scalable, high-impact modeling solutions.
- Statistical & Machine Learning Modeling: Design, develop, and refine statistical and machine learning models (e.g., time series forecasting, probabilistic models) to support decision-making and enhance product capabilities.
- Analytics Engineering & Data Modeling: Perform complex data transformations and develop well-structured data models. Translate business and analytical requirements into scalable, tested, and well-documented datasets.
- Software Development & Productionization: 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.
- 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.
What We're Looking For (Minimum Qualifications)- Master's degree in Computer Science, Statistics, Engineering, Applied Mathematics, or a related quantitative field and 2+ years of professional experience in data science or machine learning; or
- Bachelor's degree and 4+ years of professional experience in data science or machine learning
- Strong foundation in statistical modeling and machine learning, including time series forecasting and model evaluation
- Experience deploying and maintaining models in cloud-based environments (e.g., AWS, GCP, or Azure) using MLOps practices
- Strong Python expertise, including experience with scientific computing and ML libraries (e.g., NumPy, pandas, scikit-learn, PyTorch, TensorFlow)
- Hands-on experience in orchestrating complex data transformations (e.g. Airflow, Dagster, dbt)
- Strong software engineering practices (version control, testing, code reviews, CI/CD)
- Strong communication skills, with the ability to explain technical concepts to non-technical stakeholders
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 developing probabilistic forecasting models and quantifying uncertainty
- PhD in a quantitative field
Base Pay Range$146,000 - $172,000
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
