Staff Data Scientist, Insurance Pricing
New
P
Porch GroupInsurance pricing
United States; RemoteFull-TimeStaff
Salary$168,800 – $236,300 annually
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Job Details
- Experience
- 10+ years of experience in data science, with significant depth in insurance pricing or risk modeling
- Required Skills
- PythonSQLscikit-learnMLOps
Requirements
- Have 10+ years of data science experience, with significant depth in insurance pricing or risk modeling.
- Demonstrate technical leadership on complex projects, including setting direction, establishing standards, and mentoring or overseeing data scientists.
- Have experience architecting, validating, and deploying GLM-based pricing models in production.
- Bring experience with machine learning models for non-pricing use cases; homeowners or other property and casualty experience is preferred.
- Be proficient in Python and SQL.
- Have experience implementing GLMs and gradient-boosting models using tools such as scikit-learn, statsmodels, XGBoost, or LightGBM.
- Have experience with experimental design, A/B testing in high-traffic environments, and causal analysis.
- Have experience with cloud-based data platforms such as BigQuery or GCP, and MLOps practices including training pipelines, versioning, and monitoring.
- Be proficient with Confluence and Jira and familiar with Agile/Scrum environments.
- A master’s or PhD in Statistics, Mathematics, Computer Science, or a related quantitative field is preferred.
- Actuarial science or insurance mathematics experience, including regulatory model filing, is a plus.
- Experience with customer lifetime value, retention, conversion, geospatial analysis, generative AI/LLMs, causal inference, or insurance model governance is a plus.
Responsibilities
- Set technical vision, best practices, and modeling standards for insurance pricing and risk modeling.
- Provide technical oversight and mentorship to data scientists.
- Architect and deploy GLM-based frequency, severity, and loss-cost models for homeowners insurance pricing.
- Build machine learning models, including gradient-boosted and neural-network models, for underwriting accuracy and profitability.
- Develop ensemble models for insured-level profitability, retention, conversion, and customer lifetime value.
- Use aerial imagery, satellite data, government records, and building permits to quantify localized risk.
- Partner with product, actuarial, engineering, and business leaders to scope initiatives and integrate data science into workflows.
- Work with actuarial teams to develop, file, implement, and monitor predictive models that meet regulatory requirements.
- Champion deployment practices including A/B testing, performance monitoring, and continuous refinement.
- Evaluate generative AI and large language models for potential insurance pricing applications.
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