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Porch Group

Connects homeowners with local home pros

Data Science Manager - Insurance Pricing and Underwriting

Data ScienceUpdated on Nov 10
Full-timeRemote in USA

About the Job

Requirements

  • At least 8 years of data science experience, with significant depth in insurance pricing or risk modeling.
  • Experience managing or leading data scientists, including hiring, coaching, and performance management.
  • Hands-on experience building, validating, and deploying GLM-based pricing models for frequency, severity, and loss cost in production, ideally in homeowners or other property/casualty lines.
  • Proficiency in Python and SQL, with experience implementing generalized linear models and gradient boosting models using scikit-learn, statsmodels, xgboost, or lightgbm.
  • Experience using AI agents and agentic workflows for model-assisted development, and ability to guide a team in using them effectively and responsibly.
  • 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 such as BigQuery or Google Cloud Platform, and MLOps practices such as model training pipelines, versioning, and monitoring.
  • Comfort with Git-based version control and collaboration tools such as GitHub or GitLab.
  • Familiarity with Atlassian products such as Confluence and Jira for documentation and project tracking in an Agile/Scrum environment.

Responsibilities

  • Directly manage a team of three to four data scientists, including hiring, onboarding, goal setting, performance feedback, and career development.
  • Contribute to the data science roadmap for pricing and underwriting models alongside the Head of Data Science, and drive the team’s execution, balancing delivery speed, model quality, and team capacity.
  • Work with the Head of Data Science to set modeling standards and best practices, and provide technical review of model design, validation, and documentation across the team.
  • Guide development and deployment of generalized linear model-based models for frequency, severity, and loss cost, built at the peril level, for homeowners insurance pricing.
  • Oversee development of advanced components of an insurance rating program, such as territory models, limit and deductible models, and other complex rating factors.
  • Direct the development of machine learning models, including gradient boosting machines and neural networks, to drive underwriting accuracy, competitive positioning, and profitability.
  • Guide development of ensemble models predicting insured-level profitability, customer retention, and conversion to inform underwriting and marketing strategies.
  • Lead use of non-traditional data sources, including aerial imagery, satellite data, government records, and 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 predictive models that meet regulatory requirements.
  • Establish use of AI agents and agentic workflows for model-assisted development across the team, and evaluate emerging techniques including generative AI and large language models to identify competitive opportunities.

Desired Qualifications

  • A master’s or PhD in Statistics, Mathematics, Computer Science, or a related quantitative field.
  • Actuarial experience or an actuarial designation, including experience collaborating with actuarial teams on regulatory model filing.
  • Experience with territory modeling, limit and deductible modeling, and other advanced components of an insurance rating program.
  • Hands-on experience with large language models, generative AI, or agentic AI, including prompt engineering, fine-tuning, or building agent-based workflows for insurance or financial services use cases.
  • Experience with geospatial or spatial data analysis, including aerial imagery, satellite data, or property-level geographic datasets.
  • Experience with model governance, validation frameworks, and regulatory compliance in insurance.

About the Company

Porch Group

Connects homeowners with local home pros

Porch connects homeowners with local home improvement, repair, and maintenance professionals through an online marketplace. Homeowners can browse over 1,100 project types, compare quotes, and hire pros for tasks ranging from small repairs to major remodels. The platform works by listing professionals (and their leads) for a fee, offering premium tools to manage jobs, and partnering with brands and insurers for additional revenue. Porch stands out with the Porch Guarantee and Property Protection program, which provide satisfaction guarantees and protections regardless of whether the work is done by Porch’s own team or a recommended pro, along with extensive homeowner resources. The company aims to simplify finding and hiring reliable local experts, deliver peace of mind through guarantees, and grow a scalable marketplace for home services.

Company size
1,001-5,000
Company stage
IPO
Headquarters
Seattle, WA
Founded
2013

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