Astrid Ellis

I’m a data scientist working in renewable energy forecasting. I build and maintain machine learning and physics-based models for wind and solar power generation, using large-scale datasets in BigQuery and production pipelines running in cloud environments. My work spans model development, performance monitoring, data quality analysis, and pipeline reliability. I collaborate closely with engineering, operations, and market teams to ensure forecasts are accurate, actionable, and aligned with business needs. I also lead and contribute to documentation, model governance, and dashboarding for transparency and decision support. In short, I help translate complex data and modeling into insights that support clean energy planning and real-world power operations.

I have a Bachelor’s degree in Mathematical Sciences, which gave me a strong foundation in statistics, modeling, and analytical problem-solving. I also completed a Data Science Bootcamp through the University of Maryland, where I focused on practical machine learning, Python workflows, and real-world data analysis. I’m currently pursuing my M.S. in Data Science at Drexel University to deepen my expertise in advanced analytics, forecasting, and applied machine learning.