Data Scientist at Pearl Certification
Pearl rates U.S. homes’ performance with standards across five pillars.
Leads applied research and predictive modeling on residential housing and energy-performance data; improve proprietary home SCORE and energy models using causal inference, anomaly detection, outlier analysis, feature importance, and validation against field-collected data; analyze ~92M residential SCOREs; develop new metrics such as total cost of ownership; evaluate climate-risk data integration and links between resilience features and extreme events; primary technical liaison to external consultants, statistical firms, and research partners; contributes to white papers and academic/public research outputs; requires master's in a quantitative field or equivalent and 4+ years applied statistical/predictive-modeling experience with large real-world non-experimental datasets; must have hands-on causal inference, anomaly detection, Python or R, SQL, and model validation; bonus: model interpretability, housing/real-estate/energy/utility data, climate/environmental risk modeling, published research
Remote within the United States only
