<div>About the rolePearl 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<ul style='font-family:"Basel Grotesk" , Arial , sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;'><li style="margin:3px 0px;font-size:11pt;line-height:1.6;letter-spacing:0.25px;">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.</li><li style="margin:3px 0px;font-size:11pt;line-height:1.6;letter-spacing:0.25px;">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.</li><li style="margin:3px 0px;font-size:11pt;line-height:1.6;letter-spacing:0.25px;">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.</li><li style="margin:3px 0px;font-size:11pt;line-height:1.6;letter-spacing:0.25px;">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.</li><li style="margin:3px 0px;font-size:11pt;line-height:1.6;letter-spacing:0.25px;">Support development of new performance metrics (e.g., Total Cost of Ownership) by identifying and validating relevant data sources and analytical approaches.</li><li style="margin:3px 0px;font-size:11pt;line-height:1.6;letter-spacing:0.25px;">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.</li><li style="margin:3px 0px;font-size:11pt;line-height:1.6;letter-spacing:0.25px;">Serve as the primary technical point of contact for external data and statistical partners. </li><li style="margin:3px 0px;font-size:11pt;line-height:1.6;letter-spacing:0.25px;">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.</li></ul>What we are looking forRequired Qualifications, Skills, and Abilities:<ul style='font-family:"Basel Grotesk" , Arial , sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;'><li style="color:rgb(0,0,0);margin:3px 0px;font-size:11pt;line-height:1.6;list-style:none;letter-spacing:0.25px;"><ul style='font-family:"Basel Grotesk" , Arial , sans-serif;font-size:11pt;font-weight:400;margin-left:0px;margin-right:0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:circle;'><li style="color:rgb(0,0,0);margin:12pt 0px 0pt;font-size:12pt;line-height:1.38;letter-spacing:0.25px;">Master's degree in Statistics, Economics, Data Science, Applied Mathematics, or a related quantitative field (or equivalent experience)</li><li style="color:rgb(0,0,0);margin:0pt 0px;font-size:12pt;line-height:1.38;letter-spacing:0.25px;">4+ years of applied experience in statistical analysis and predictive modeling, ideally involving large, real-world (non-experimental) datasets</li><li style="color:rgb(0,0,0);margin:0pt 0px;font-size:12pt;line-height:1.38;letter-spacing:0.25px;">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</li><li style="color:rgb(0,0,0);margin:0pt 0px;font-size:12pt;line-height:1.38;letter-spacing:0.25px;">Experience with anomaly detection and outlier analysis techniques applied to large datasets</li><li style="color:rgb(0,0,0);margin:0pt 0px;font-size:12pt;line-height:1.38;letter-spacing:0.25px;">Strong proficiency in a statistical/analytical programming language (Python or R) and SQL</li><li style="color:rgb(0,0,0);margin:0pt 0px;font-size:12pt;line-height:1.38;letter-spacing:0.25px;">Experience validating model outputs against ground-truth or field-collected data</li><li style="color:rgb(0,0,0);margin:0pt 0px;font-size:12pt;line-height:1.38;letter-spacing:0.25px;">Ability to translate statistical findings into clear, non-technical explanations for internal stakeholders and external partners</li><li style="color:rgb(0,0,0);margin:0pt 0px 12pt;font-size:12pt;line-height:1.38;letter-spacing:0.25px;">Experience working directly with external consultants, research firms, or academic partners on collaborative analytical projects</li></ul></li></ul>Preferred Qualifications (What makes you stand out)<ul style='font-family:"Basel Grotesk" , Arial , sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;'><li style="color:rgb(0,0,0);margin:3px 0px;font-size:11pt;line-height:1.6;list-style:none;letter-spacing:0.25px;"><ul style='font-family:"Basel Grotesk" , Arial , sans-serif;font-size:11pt;font-weight:400;margin-left:0px;margin-right:0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:circle;'><li style="color:rgb(0,0,0);margin:12pt 0px 0pt;font-size:11pt;line-height:1.38;letter-spacing:0.25px;">Experience with feature importance analysis and model interpretability techniques</li><li style="color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;letter-spacing:0.25px;">Familiarity with housing, real estate, energy, or utility data (assessor records, permit data, utility billing, energy modeling)</li><li style="color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;letter-spacing:0.25px;">Experience integrating or evaluating climate/environmental risk data into predictive models</li><li style="color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;letter-spacing:0.25px;">A track record of authoring or co-authoring published research</li><li style="color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;letter-spacing:0.25px;">Experience working with ambiguity and scale - large datasets, real-world conditions, innovative methodology</li><li style="color:rgb(0,0,0);margin:0pt 0px 12pt;font-size:11pt;line-height:1.38;letter-spacing:0.25px;">Comfortable working semi-independently, with support and partnerships</li></ul></li></ul>Why work at Pearl?<ul style='font-family:"Basel Grotesk" , Arial , sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;'><li style="margin:3px 0px;font-size:11pt;line-height:1.6;letter-spacing:0.25px;">We are a mission-driven company: we love what we do and the impact we are making.</li><li style="margin:3px 0px;font-size:11pt;line-height:1.6;letter-spacing:0.25px;">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.</li><li style="margin:3px 0px;font-size:11pt;line-height:1.6;letter-spacing:0.25px;">Flexibility. We are 100% remote - work where you feel comfortable.</li><li style="margin:3px 0px;font-size:11pt;line-height:1.6;letter-spacing:0.25px;">Environment. We value candor, excellence, and collaboration while fostering creativity and camaraderie. We are supportive and genuinely enjoy celebrating each others’ wins!</li><li style="margin:3px 0px;font-size:11pt;line-height:1.6;letter-spacing:0.25px;">Ownership. You will hold broad responsibilities with high autonomy in a fast-paced, evolving startup world.</li><li style="margin:3px 0px;font-size:11pt;line-height:1.6;letter-spacing:0.25px;">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.</li></ul>Compensation and Benefits:<ul style='font-family:"Basel Grotesk" , Arial , sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;'><li style="margin:3px 0px;font-size:12pt;line-height:1.6;letter-spacing:0.25px;">Salary expected in the range of 155k-175k, based on candidate experience and local market conditions</li><li style="margin:3px 0px;font-size:12pt;line-height:1.6;letter-spacing:0.25px;">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)</li><li style="margin:3px 0px;font-size:12pt;line-height:1.6;letter-spacing:0.25px;">FSA, HSA, and dependent care accounts</li><li style="margin:3px 0px;font-size:12pt;line-height:1.6;letter-spacing:0.25px;">Life insurance coverage</li><li style="margin:3px 0px;font-size:12pt;line-height:1.6;letter-spacing:0.25px;">Employer paid cell phone service</li><li style="margin:3px 0px;font-size:12pt;line-height:1.6;letter-spacing:0.25px;">401(k) with employer match up to 4%</li><li style="margin:3px 0px;font-size:12pt;line-height:1.6;letter-spacing:0.25px;">Stock options</li><li style="margin:3px 0px;font-size:12pt;line-height:1.6;letter-spacing:0.25px;">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</li><li style="margin:3px 0px;font-size:12pt;line-height:1.6;letter-spacing:0.25px;">Flexible work environment: work remotely from anywhere within the U.S.</li></ul> </div>

Pearl is a national start-up that certifies high-performing homes: homes with efficient heating and cooling, solar, smart home devices, resilient features and much more. Pearl&rsquo;s comprehensive certification drives demand for these improvements by capturing their value for resale and appraisal, benefiting homeowners and the contractors, builders, raters, real estate professionals, and public entities who serve them. Pearl's products are de-risked, in-market, and scaling ahead of nationwide adoption that will be accelerated by the rollout of the Inflation Reduction Act, which directs nearly $400 billion in federal funding to clean energy.
Pearl&rsquo;s mission is to make our global community a better place by giving homeowners the tools to improve their homes&rsquo; health, comfort, efficiency, resilience &mdash; and value. We are passionate about improving the performance of residential buildings as a way to improve people&rsquo;s lives and reduce greenhouse gas emissions.
Please note that Pearl is a completely remote team.&nbsp; We encourage people from all over the U.S. to apply for ANY position, even if it is listed as "Remote (city, state)".&nbsp;&nbsp;

Data Scientist

<div>About the rolePearl 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<ul><li>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.</li><li>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.</li><li>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.</li><li>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.</li><li>Support development of new performance metrics (e.g., Total Cost of Ownership) by identifying and validating relevant data sources and analytical approaches.</li><li>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.</li><li>Serve as the primary technical point of contact for external data and statistical partners. </li><li>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.</li></ul>What we are looking forRequired Qualifications, Skills, and Abilities:<ul><li><ul><li>Master's degree in Statistics, Economics, Data Science, Applied Mathematics, or a related quantitative field (or equivalent experience)</li><li>4+ years of applied experience in statistical analysis and predictive modeling, ideally involving large, real-world (non-experimental) datasets</li><li>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</li><li>Experience with anomaly detection and outlier analysis techniques applied to large datasets</li><li>Strong proficiency in a statistical/analytical programming language (Python or R) and SQL</li><li>Experience validating model outputs against ground-truth or field-collected data</li><li>Ability to translate statistical findings into clear, non-technical explanations for internal stakeholders and external partners</li><li>Experience working directly with external consultants, research firms, or academic partners on collaborative analytical projects</li></ul></li></ul>Preferred Qualifications (What makes you stand out)<ul><li><ul><li>Experience with feature importance analysis and model interpretability techniques</li><li>Familiarity with housing, real estate, energy, or utility data (assessor records, permit data, utility billing, energy modeling)</li><li>Experience integrating or evaluating climate/environmental risk data into predictive models</li><li>A track record of authoring or co-authoring published research</li><li>Experience working with ambiguity and scale - large datasets, real-world conditions, innovative methodology</li><li>Comfortable working semi-independently, with support and partnerships</li></ul></li></ul>Why work at Pearl?<ul><li>We are a mission-driven company: we love what we do and the impact we are making.</li><li>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.</li><li>Flexibility. We are 100% remote - work where you feel comfortable.</li><li>Environment. We value candor, excellence, and collaboration while fostering creativity and camaraderie. We are supportive and genuinely enjoy celebrating each others’ wins!</li><li>Ownership. You will hold broad responsibilities with high autonomy in a fast-paced, evolving startup world.</li><li>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.</li></ul>Compensation and Benefits:<ul><li>Salary expected in the range of 155k-175k, based on candidate experience and local market conditions</li><li>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)</li><li>FSA, HSA, and dependent care accounts</li><li>Life insurance coverage</li><li>Employer paid cell phone service</li><li>401(k) with employer match up to 4%</li><li>Stock options</li><li>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</li><li>Flexible work environment: work remotely from anywhere within the U.S.</li></ul> </div>

Data Scientist

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