A Master's degree in Statistics, Economics, Data Science, Applied Mathematics, or a related quantitative field, or equivalent experience.
At least four years of applied experience in statistical analysis and predictive modeling, ideally involving large, real-world non-experimental datasets.
Hands-on experience with causal inference methods, including regression analysis, propensity score matching, and instrumental variable approaches, and understanding when correlation-based methods are insufficient to support causal claims.
Experience with anomaly detection and outlier analysis techniques applied to large datasets.
Strong proficiency in a statistical or analytical programming language, such as 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.
Experience working directly with external consultants, research firms, or academic partners on collaborative analytical projects.
Responsibilities
Lead advanced research and predictive modeling focused on residential housing and energy performance data.
Manage research with external consultants and statistical firms to identify correlations and causal relationships between home performance data and other housing-related data, such as energy cost and mortgage performance, using regression analysis, propensity score matching, and, where data permit, instrumental variable methods; ensure causal claims are supported by appropriate causal inference methods rather than inferred from controlled regression alone.
Analyze approximately 92 million residential SCOREs and energy models to identify homes where the SCORE or model output may not accurately reflect physical configuration or energy consumption, using anomaly detection, outlier analysis, and validation against field-collected home-characteristics data.
Analyze modeled energy consumption, home physical characteristics, and utility billing data to identify and implement improvements to predictive accuracy of Pearl’s energy models.
Analyze relationships between field-collected home performance characteristics and SCORE outputs to identify opportunities to improve SCORE accuracy, using feature-importance analysis and comparison against field-validated benchmarks.
Support development of new performance metrics, such as Total Cost of Ownership, by identifying and validating relevant data sources and analytical approaches.
Evaluate opportunities to integrate climate-risk data into the SCORE to improve predictive precision around homes’ climate vulnerability, and analyze relationships between homes’ resilience features and ability to withstand extreme climate events.
Serve as the primary technical point of contact for external data and statistical partners.
Assist with authoring white papers, briefs, and other publications documenting the research for publication on Pearl’s research page, academic journals, and similar outlets.
Desired Qualifications
Experience with feature-importance analysis and model-interpretability techniques.
Familiarity with housing, real estate, energy, or utility data, including assessor records, permit data, utility billing, and energy modeling.
Experience integrating or evaluating climate or environmental risk data in predictive models.
A track record of authoring or co-authoring published research.
Experience working with ambiguity and scale, including large datasets, real-world conditions, and innovative methodology.
Comfort working semi-independently, with support and partnerships.
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. across five key pillars — Safety, Comfort, Operations, Resilience, and Energy — so homebuyers, homeowners, and real estate professionals can understand how a home performs in daily life.
Today, the value of a home is judged by its location, size, and aesthetics— but these factors overlook the home’s most important function: safeguarding the comfort, well-being, and finances of the people inside. Most American homes underperform in these regards because home performance is simply not on our radar. We can’t see it, quantify it, or put a value on it. Pearl aims to change that by making home performance a central part of how we view and value our homes.
We are not a home inspector, a contractor, a real estate platform, or a green-certification program — we rate homes against a transparent, evidence-based standard so that home performance becomes visible, measurable, and valuable in the real estate market.
As a Certified B Corporation, we're accountable not only to our shareholders, but also to the homeowners and communities we serve. For more information, visit pearlscore.com.
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 accurately reflect the home's actual physical configuration or energy consumption, using anomaly detection, outlier analysis, and validation against field-collected data on home characteristics.
Analyze modeled energy consumption, home physical characteristics, and utility billing data to identify and implement improvements to the predictive accuracy of Pearl's energy models.
Analyze relationships between field-collected home performance characteristics and SCORE outputs to identify opportunities to improve SCORE accuracy, using techniques such as feature importance analysis and comparison against field-validated benchmarks.
Support development of new performance metrics (e.g., Total Cost of Ownership) by identifying and validating relevant data sources and analytical approaches.
Evaluate opportunities to integrate climate risk data into the SCORE, to improve predictive precision around homes’ climate vulnerability, and to analyse the relationships between homes’ resilience features and ability to withstand extreme climate events.
Serve as the primary technical point of contact for external data and statistical partners.
Assist with the authorship of white papers, briefs, and other publications documenting the research described above for publication on Pearl’s research page, academic journals, etc.
What we are looking for
Required Qualifications, Skills, and Abilities:
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
Demonstrated hands-on experience with causal inference methods — regression analysis, propensity score matching, and instrumental variable approaches — and a clear understanding of when correlation-based methods are and are not sufficient to support causal claims
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
Experience working directly with external consultants, research firms, or academic partners on collaborative analytical projects
Preferred Qualifications (What makes you stand out)
Experience with feature importance analysis and model interpretability techniques
Familiarity with housing, real estate, energy, or utility data (assessor records, permit data, utility billing, energy modeling)
Experience integrating or evaluating climate/environmental risk data into predictive models
A track record of authoring or co-authoring published research
Experience working with ambiguity and scale - large datasets, real-world conditions, innovative methodology
Comfortable working semi-independently, with support and partnerships
Why work at Pearl?
We are a mission-driven company: we love what we do and the impact we are making.
Impact. Everything you do here will matter. Your opinion and contributions will make a big difference to the future of this company and our mission of making home performance matter.
Flexibility. We are 100% remote - work where you feel comfortable.
Environment. We value candor, excellence, and collaboration while fostering creativity and camaraderie. We are supportive and genuinely enjoy celebrating each others’ wins!
Ownership. You will hold broad responsibilities with high autonomy in a fast-paced, evolving startup world.
Equality between people. We support diversity, championing our differences, and most importantly, learn from one another. Pearl is an equal opportunity employer, and candidates from all backgrounds and life experiences are encouraged to apply.
Compensation and Benefits:
Salary expected in the range of 155k-175k, based on candidate experience and local market conditions
Medical, vision and dental coverage provided at no cost for employees and their families(with an option to purchase upgraded coverage at a minimal cost to employee)
FSA, HSA, and dependent care accounts
Life insurance coverage
Employer paid cell phone service
401(k) with employer match up to 4%
Stock options
15 vacation days during the calendar year, plus holidays (including the week between Christmas and New Year’s Day), a floating holiday for your birthday, sick days, and paid parental leave
Flexible work environment: work remotely from anywhere within the U.S.