Staff Data Scientist (Insurance Risk and Pricing)

Porch Group • Seattle, WA

3d • $168,800 - $236,300 • Remote

About The Position

Porch Group is a leading vertical software and insurance platform focused on the home-buying transaction. They offer differentiated products and services, with homeowners insurance at the center. Porch Group has built relationships with approximately 30,000 companies in the home-buying transaction space and began trading on the Nasdaq in 2020. The company is looking for a Staff Data Scientist, Insurance Pricing to join their team. This role is for a senior individual contributor who will set technical direction for insurance pricing and risk modeling, owning complex modeling problems from strategy to deployment. The role involves GLM-based pricing, profitability and retention modeling, geospatial risk analysis, and exploring emerging techniques like generative AI. The Staff Data Scientist will partner with actuarial, product, and engineering leaders to leverage property data for competitive advantage in underwriting and pricing. This is an ideal role for a seasoned practitioner aiming to shape strategy, mentor others, and drive business value.

Requirements

• 10+ years of experience in data science, with significant depth in insurance pricing or risk modeling
• Track record of technical leadership — setting direction on complex projects, establishing standards, and mentoring or providing technical oversight to other data scientists
• Demonstrated expertise architecting, 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 Haves

• 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

Responsibilities

• Set the technical vision for insurance pricing and risk modeling, establishing best practices and modeling standards across the team
• Provide technical oversight and mentorship to other data scientists
• Architect 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
• Lead use of non-traditional data sources — aerial imagery, satellite data, government records, building permits — to quantify localized risk and inform strategic decisions
• Partner with product, actuarial, engineering, and business leaders to scope high-priority 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
• Champion rigorous deployment practices in high-traffic environments, including A/B testing, performance monitoring, and continuous refinement
• Drive a culture of experimentation, evaluating emerging techniques including generative AI and LLMs to identify new opportunities for competitive advantage

Benefits

• Pay Range: $168,800 – $236,300 annually
• Long-term incentive awards
• Three (3) Medical plan options
• Two (2) Dental plan options
• Vision plan
• Voluntary Critical Illness, Hospital Indemnity and Accident plans
• Pre-tax savings options including a partially employer funded Health Savings Account
• Employee Flexible Savings Accounts including healthcare, dependent care, and transportation savings options
• Company paid Basic Life and AD&D
• Short and Long-Term Disability benefits
• Voluntary Life and AD&D plans
• Traditional and Roth 401(k) plans with a discretionary employer match
• Wellbeing program (Supportlinc) providing access to guided meditation, mindfulness exercises, mental health coaching, clinical care, and will preparation resources
• LifeBalance resource for discounts on gym memberships, travel, appliances, movies, pet insurance and more
• Flexible paid vacation
• Company-paid holidays (typically nine per year)
• Paid sick time
• Paid parental leave
• Identity theft program
• Travel assistance
• Fitness and other discounts programs

What This Job Offers

• Job Type: Full-time
• Career Level: Senior