> Markdown version of [/jobs/ext/4639673-senior-data-scientist](https://www.wearedevelopers.com/jobs/ext/4639673-senior-data-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Senior Data Scientist

- **Company:** Porch
- **Location:** Seattle, WA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $138,800 - $194,300
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Artificial Neural Networks, Confluence, JIRA, BigQuery, Code Review, Generalized Linear Model, Python (Programming Language), Machine Learning, Scrum Methodology, Software Deployment, Spatial Data Infrastructures, SQL Databases, Management of Software Versions, Cloud Platform System, Large Language Models, Prompt Engineering, Generative AI, Scikit Learn, Information Technology, Statistics Packages, Xgboost, Performance Monitor, Machine Learning Operations
- **Published:** November 8, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=c0a2393aa14ff166

## About the Role

* 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 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
*

## Description

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
*