About Pearl Founded in 2013, Pearl is a ratings and standards company building the national standard for home performance. Pearl SCORE™ rates every single-family home in the U.S. Backed by Clean Energy Ventures.
About the role
Pearl is seeking a Data Scientist to lead advanced research and predictive modeling, focusing on analyzing residential housing and energy performance data. The role involves utilizing causal inference and anomaly detection techniques to improve the accuracy of Pearl’s proprietary SCORE models and developing new performance metrics. Additionally, this position acts as the primary technical liaison for external research partners and contributes to the dissemination of findings through white papers and publications
What they're looking for
- Master's degree in Statistics, Economics, Data Science, Applied Mathematics, or a related quantitative field (or equivalent experience)
- 4+ years of applied experience in statistical analysis and predictive modeling, ideally involving large, real-world (non-experimental) datasets
- Experience with anomaly detection and outlier analysis techniques applied to large datasets
- Strong proficiency in a statistical/analytical programming language (Python or R) and SQL
- Experience validating model outputs against ground-truth or field-collected data
- Ability to translate statistical findings into clear, non-technical explanations for internal stakeholders and external partners
More about this role
About the role
Pearl is seeking a Data Scientist to lead advanced research and predictive modeling, focusing on analyzing residential housing and energy performance data. The role involves utilizing causal inference and anomaly detection techniques to improve the accuracy of Pearl’s proprietary SCORE models and developing new performance metrics. Additionally, this position acts as the primary technical liaison for external research partners and contributes to the dissemination of findings through white papers and publications
What you'll do
- Manage research, conducted in partnership with external consultants and statistical firms, that identifies correlations and causal relationships between home performance data and other housing-related data (e.g., energy cost and mortgage performance), using techniques such as regression analysis, propensity score matching, and (where data permit) instrumental variable methods, and ensuring causal claims are supported by appropriate causal inference methods rather than inferred from controlled regression alone.
- Analyze Pearl's ~92 million residential SCOREs and energy models to identify homes where the SCORE or model output is unlikely to...
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