Senior Data Scientist

Overview

Senior Data Scientist, Insurance Pricing, owning the development and deployment of homeowners insurance pricing and risk models from scoping through production, with GLM-based pricing at the core and contribution to profitability, retention, geospatial risk and generative AI evaluation.

What you'll do

• 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'll need

• 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

Nice to have

• Master's or PhD in Statistics, Mathematics, Computer Science, or a related quantitative field
• Background in actuarial science or insurance mathematics, including experience collaborating with actuarial teams on regulatory model filing
• Experience with customer lifetime value, retention, or conversion modeling
• Experience with geospatial or spatial data analysis (aerial imagery, satellite data, or property-level geographic datasets)
• Exposure to generative AI and LLMs, including prompt engineering or fine-tuning for insurance or financial services use cases
• Exposure to experimentation frameworks and causal inference methods
• Experience with model governance, validation frameworks, and regulatory compliance in insurance

Details

• Workplace type: Remote, United States
• Not open to applicants from Alaska, Delaware, Hawaii, Mississippi, Nebraska, Montana, New Hampshire, West Virginia, or the District of Columbia
• Application window anticipated to close 10 business days from 11/6/2026, subject to change