Data Scientist, Forecasting – Energy Storage System Optimization

Responsibilities

• Develop, implement, and deploy machine learning models and statistical analyses to optimize energy storage and solar asset performance across Stem's portfolio

• Design and build data pipelines to extract, transform, and analyze large-scale datasets from energy systems

• Collaborate with software engineering, product management, and energy experts to integrate data-driven solutions into PowerTrack platforms

• Create data visualizations, dashboards, and reports to communicate insights and recommendations to stakeholders

• Conduct exploratory data analysis to identify trends, patterns, and opportunities for improving system performance and business outcomes

Requirements

• Minimum of 1-3 years in data science, analytics, machine learning, or related field

• Bachelor's degree in Data Science, Statistics, Computer Science, Mathematics, Engineering, or related field, or equivalent experience

• Proficiency in Python and data science libraries (pandas, numpy, scikit-learn, TensorFlow or PyTorch)

• Familiarity with optimization libraries (e.g. CVXPY) and experience formulating or interpreting constrained optimization problems

• Strong SQL skills and experience working with large datasets using SQL or SQL-like query languages.

• Comfortable using modern AI coding assistants (Cursor, Claude Code, GitHub Copilot, etc.) to accelerate development

• Experience writing well documented, well tested, and maintainable code

• Version control with Git and experience using GitHub, GitLab, or Bitbucket

• Experience prototyping analyses in Jupyter notebooks and building lightweight data applications to explore and communicate insights, using tools such as Matplotlib, Seaborn, Plotly, and frameworks like Streamlit or Dash.

• Strong analytical and problem-solving skills with expertise in statistical methods and machine learning algorithms

• Excellent communication and presentation abilities with capacity to translate complex technical findings for diverse audiences.