Data Scientist

GridCARE solves data center developers' most urgent bottleneck — immediate access to power — through a pioneering physics-based generative AI platform that unlocks gigawatts of near-term capacity from today’s grid. Partnering with utilities, GridCARE applies a proprietary playbook to create additional network capacity on existing transmission infrastructure without costly upgrades or multi-year delays. Founded at Stanford’s Doerr School and backed by leading investors in Energy and AI, GridCARE is working with major technology and data center companies to accelerate interconnection requests for large-load and data center projects.

As a fast-growing startup, we are seeking a skilled Data Scientist to help develop data-driven solutions that have real-world impact.

We are seeking a Data Scientist who thrives at the intersection of business and engineering — someone who can translate complex technical data into clear, compelling insights for both internal and external stakeholders.

In this role, you’ll collaborate closely with GridCARE’s business and engineering teams to produce high-quality visualizations and analyses that support customer engagements and real-world project delivery. Your work will include creating interactive maps, dashboards, and analytical visuals that showcase project outcomes, grid capacity opportunities, and results from power system studies.

This is a unique opportunity to combine deep data science and visualization expertise with cutting-edge AI solutions applied to solve the most impactful speed to power in the energy industry.

• →

• Bachelor’s or higher in Computer Science, Data Science, Electrical Engineering, or a related quantitative field.

• Strong coding skills in Python or equivalent, including experience with data manipulation libraries (e.g., Pandas, NumPy) and database interaction.

• Ability to write well-documented, tested, and maintainable code.

• Experience in applying machine-learning and AI applications to data analysis.

• Knowledge of time-series analysis techniques and non-relational database concepts.

• Able to leverage AI code development tools such as Cursor, Copilot, and Claude Code to accelerate development.

• Excellent problem-solving, communication, and collaboration skills in a fast-paced startup environment.

• 2+ years of experience in data engineering, data science, or related areas.

• Experience with Model Context Protocol (MCP) and other multi-agent architectures.

• Proficiency with cloud data platforms (e.g., AWS, GCP, Azure) and data warehousing solutions.

• Familiar with data modeling, ETL processes, and data governance.

• Familiar with RESTful APIs and Python web frameworks such as Flask and FastAPI.

• Experience in the energy industry, including grid planning and operations.

• Experience with time-series forecasting.

• Experience deploying data applications in the cloud.

• Familiarity with version control (git) and basic software engineering practices.