Senior Data Scientist

Porch.com; United States; Remote; USD 150,000 - 210,000; Full time

Job summary

Porch Group is seeking a Senior Data Scientist, Insurance Pricing in the United States, with ownership of pricing and risk models for homeowners insurance. You will work with actuarial, product, and engineering teams to translate property data into pricing advantages and profitability insights.

The role emphasizes GLM-based pricing, ensemble approaches, and exploring generative AI as a competitive edge, while applying rigorous deployment and monitoring practices.

Qualifications

• 5+ years of data science experience with emphasis on pricing or risk modeling.
• Experience building, validating, deploying GLM-based pricing models in production.
• Proficiency in Python and SQL; familiarity with GLMs and gradient boosting models.
• Experience with cloud data platforms and MLOps practices.

Responsibilities

• Contribute to modeling standards and best practices for insurance pricing and risk modeling.
• Mentor data scientists through code reviews and knowledge sharing.
• Build, validate, and deploy GLM-based pricing models (frequency, severity, loss cost).
• Develop ML models (GBMs, neural nets) to improve underwriting profitability and pricing.
• Create ensemble models for profitability, retention, and LTV-based strategies.
• Incorporate aerial imagery, satellite and other non-traditional data sources for risk.
• Collaborate with product, actuarial, engineering, and business stakeholders to deploy solutions.
• Work with actuarial teams to file and monitor regulatory models.
• Adhere to deployment practices with A/B testing and monitoring.

Skills

Python

SQL

GLMs

Gradient boosting

GBMs

A/B testing

BigQuery

GCP

MLOps

Jira

Confluence

Actuarial collaboration

LTV modeling

Geospatial data

xgboost

lightgbm

Education

Master's or PhD in Statistics/Mathematics/Computer Science

Tools

scikit-learn

statsmodels

xgboost

lightgbm

Jira

Confluence

BigQuery

Job description

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: Senior 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 Senior Data Scientist, Insurance Pricing. As a senior member of our data science team, you will own the development and deployment of our homeowners insurance pricing and risk models, taking complex modeling problems from scoping through production deployment. While GLM-based pricing sits at the core of this role, you will also contribute to profitability and retention modeling, geospatial risk analysis, and the evaluation of emerging techniques like generative AI. You'll partner closely with actuarial, product, and engineering teams to translate Porch's unique property data into a durable competitive advantage in underwriting and pricing. This is an ideal role for an experienced practitioner who wants to deepen their technical expertise, support other data scientists, and drive measurable business value.

What You Will Do As A Senior Data Scientist, Insurance Pricing

• Contribute to modeling standards and best practices for insurance pricing and risk modeling, helping the team apply them consistently
• Support and mentor junior data scientists through code review, knowledge sharing, and technical guidance
• Build, validate, 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
• Incorporate non-traditional data sources, including aerial imagery, satellite data, government records, and building permits, to quantify localized risk and inform pricing and underwriting decisions
• Partner with product, actuarial, engineering, and business stakeholders to scope 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
• Follow rigorous deployment practices in high-traffic environments, including A/B testing, performance monitoring, and continuous refinement
• Evaluate emerging techniques, including generative AI and LLMs, to identify new opportunities for competitive advantage

What You Will Bring As A Senior Data Scientist, Insurance Pricing

• 5+ years of experience in data science, with significant depth in insurance pricing or risk modeling
• Track record of technical ownership, delivering complex projects end-to-end and supporting or mentoring less-experienced data scientists
• Demonstrated experience building, 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 hav