Staff Data Scientist (Insurance Risk and Pricing)

Porch Group is a leading vertical software and insurance platform and is positioned to be the best partner to help homebuyers move, maintain, and fully protect their homes. We offer differentiated products and services, with homeowners insurance at the center of this relationship. We differentiate and look to win in the massive and growing homeowners insurance opportunity by 1) providing the best services for homebuyers, 2) led by advantaged underwriting in insurance, 3) to protect the whole home. As a leader in the home services software-as-a-service (“SaaS”) space, we’ve built deep relationships with approximately 30 thousand companies that are key to the home-buying transaction, such as home inspectors, mortgage companies, and title companies. In 2020, Porch Group rang the Nasdaq bell and began trading under the ticker symbol PRCH. We are looking to build a truly great company and are JUST GETTING STARTED. Job Title:   Staff Data Scientist, Insurance Pricing   Location:   United States   Workplace Type:   Remote   Job Summary   The future is bright for the Porch Group, and   we’d   love for   you to be a part of it as our Staff Data Scientist, Insurance Pricing.   As one of the most senior individual contributors on our data science team, you will set technical direction for how Porch approaches homeowners insurance pricing and risk modeling, owning our most complex and ambiguous modeling problems end-to-end — from business strategy through production deployment. While GLM-based pricing sits at the core of this role, your influence will extend across profitability and retention modeling, geospatial risk analysis, and the adoption of emerging techniques like generative AI.   You’ll   partner closely with actuarial, product, and engineering leaders to translate Porch’s unique property data into a durable competitive advantage in underwriting and pricing. This is an ideal role for a seasoned practitioner who wants to shape strategy, mentor other data scientists, and drive measurable business value at scale.   What You Will Do   As   A   Staff Data Scientist, Insurance Pricing   Set the technical vision for insurance pricing and risk modeling,   establishing   best practices and modeling standards across the team   Provide technical oversight and mentorship to other data scientists   Architect and deploy GLM-based models (frequency, severity, and loss cost) for homeowners insurance pricing   Build machine learning models (GBMs, neural networks, etc.) that drive underwriting accuracy, competitive positioning, and profitability   Develop ensemble models predicting insured-level profitability, customer retention, and conversion, including customer lifetime value (LTV) models to prioritize marketing and underwriting strategies   Lead use of non-traditional data sources — aerial imagery, satellite data, government records, building permits — to quantify localized risk and inform strategic decisions   Partner with product, actuarial, engineering, and business leaders to scope high-priority initiatives and integrate data science solutions into operational workflows   Work with the actuarial team to develop, file, implement, and   monitor   new predictive models that meet regulatory requirements   Champion rigorous deployment practices in high-traffic environments, including A/B testing, performance monitoring, and continuous refinement   Drive a culture of experimentation, evaluating emerging techniques including generative AI and LLMs to   identify   new opportunities for competitive advantage   What You Will Bring   As   A   Staff Data Scientist, Insurance Pricing   10+ years of experience in data science, with significant depth in insurance pricing or risk modeling   Track record   of technical leadership — setting direction on complex projects,   establishing   standards, and mentoring or providing technical oversight to other data scientists   Demonstrated   expertise   architecting,   validating , and deploying GLM-based pricing models in production, ideally for homeowners or other property/casualty lines, as well as machine learning models for non-pricing use cases   Proficiency   in Python and SQL, with experience implementing GLMs and gradient boosting models (scikit-learn,   statsmodels ,   xgboost ,   lightgbm )   Experience with experimental design, including building, deploying, and A/B testing models in high-traffic environments, as well as causal analysis   Experience with cloud-based data platforms (e.g.,   BigQuery , GCP) and   MLOps   practices such as model training pipelines, versioning, and monitoring   Proficiency   with Confluence and Jira for documentation and project tracking in an Agile/Scrum environment   Master’s or PhD in Statistics, Mathematics, Computer Science, or a related quantitative field preferred   Nice to   have:   background in actuarial science or insurance mathematics, including experience collaborating with actuarial teams on regulatory model filing   Nice to   have:   experience with customer lifetime value, retention, or conversion modeling   Nice to   have:   experience with geospatial or spatial data analysis (aerial imagery, satellite data, or property-level geographic datasets)   Nice to   have:   exposure to generative AI and LLMs, including prompt engineering or fine-tuning for insurance or financial services use cases   Nice to   have:   exposure to experimentation frameworks and causal inference methods   Nice to   have:   experience with model governance, validation frameworks, and regulatory compliance in insurance   The application window for this position is   anticipated   to close in 2 weeks (10 business days) from 8/27/2026. Please   know   this may change based on business and interviewing needs.   At this time , Porch Group does not consider applicants from the following states or   jurisdictions   for Remote positions: Alaska, Delaware, H