Staff Data Scientist – Insurance Risk, Pricing

This is a fully remote position, open to applicants in Washington.

📋 Description

• Define the technical strategy for insurance pricing and risk modeling.

• Implement best practices and modeling standards throughout the data science team.

• Offer technical guidance and mentorship to fellow data scientists.

• Design and deploy GLM-based models for frequency, severity, and loss-cost in homeowners insurance pricing.

• Create machine learning models, such as GBMs and neural networks, to enhance underwriting precision, competitive edge, and profitability.

• Develop ensemble models focused on insured-level profitability, customer retention, conversion, and customer lifetime value.

• Lead the application of aerial imagery, satellite data, government records, and building permits to assess localized risk.

• Collaborate with product, actuarial, engineering, and business leaders to outline initiatives and incorporate data science solutions into operational processes.

• Work alongside actuarial teams to create, file, implement, and monitor predictive models that comply with regulatory standards.

• Advocate for deployment practices such as A/B testing, performance monitoring, and ongoing refinement.

• Assess emerging methodologies, including generative AI and LLMs, for potential competitive benefits.

⛳️ Requirements

• Over 10 years of experience in data science, with substantial expertise in insurance pricing or risk modeling.

• Proven record of technical leadership, guiding complex projects, establishing standards, and mentoring other data scientists.

• Proficiency in architecting, validating, and deploying GLM-based pricing models in production, preferably for homeowners or other property/casualty sectors.

• Familiarity with machine learning models for applications beyond pricing.

• Strong skills in Python and SQL.

• Experience implementing GLMs and gradient boosting models using scikit-learn, statsmodels, xgboost, and lightgbm.

• Background in experimental design, model deployment, A/B testing in high-traffic environments, and causal analysis.

• Experience with cloud-based data platforms such as BigQuery and GCP.

• MLOps experience, encompassing model training pipelines, versioning, and monitoring.

• Competence with Confluence and Jira.

• Experience in an Agile/Scrum environment.

• A Master’s or PhD in Statistics, Mathematics, Computer Science, or a related quantitative discipline is preferred.

• Nice to have: experience in actuarial science or insurance mathematics, and collaboration on regulatory model filings.

• Nice to have: experience in customer lifetime value, retention, or conversion modeling.

• Nice to have: experience in geospatial or spatial data analysis.

• Nice to have: exposure to generative AI and LLMs, including prompt engineering or fine-tuning.

• Nice to have: familiarity with experimentation frameworks and causal inference methods.

• Nice to have: knowledge of model governance, validation frameworks, and regulatory compliance in the insurance sector.

• Applicants must be authorized to work in the US; Porch Group does not consider candidates from Alaska, Delaware, Hawaii, Mississippi, Nebraska, Montana, New Hampshire, West Virginia, or the District of Columbia for remote roles.

🏝️ Benefits

• Long-term incentive awards, subject to program guidelines and approvals.

• Three medical plan options.

• Two dental plan options.

• Vision plan.

• Voluntary Critical Illness, Hospital Indemnity, and Accident plans.

• Partially employer-funded Health Savings Account.

• Flexible Spending Accounts for healthcare, dependent care, and transportation.

• Company-paid Basic Life and AD&D insurance.

• Company-paid Short- and Long-Term Disability benefits.

• Voluntary Life and AD&D plans.

• Traditional and Roth 401(k) plans with discretionary employer matching.

• Employer-funded Supportlinc wellbeing program offering guided meditation, mindfulness exercises, mental health coaching, clinical care, and confidential resources.

• LifeBalance discounts on gym memberships, travel, appliances, movies, pet insurance, and more.

• Flexible paid vacation.

• Typically nine company-funded holidays annually.

• Paid sick leave.

• Paid parental leave.

• Identity theft protection program.

• Travel assistance.

• Fitness and various discount programs.

• Remote work options.