[Hiring] Staff Data Scientist @Porch Group

Role Description

The future is bright for the Porch Group, and we’d love for you to be a part of it as our Staff Data Scientist, Insurance Pricing. As one of the most senior individual contributors on our data science team, you will set technical direction for how Porch approaches homeowners insurance pricing and risk modeling, owning our most complex and ambiguous modeling problems end-to-end — from business strategy through production deployment. While GLM-based pricing sits at the core of this role, your influence will extend across profitability and retention modeling, geospatial risk analysis, and the adoption of emerging techniques like generative AI.

You’ll partner closely with actuarial, product, and engineering leaders to translate Porch’s unique property data into a durable competitive advantage in underwriting and pricing. This is an ideal role for a seasoned practitioner who wants to shape strategy, mentor other data scientists, and drive measurable business value at scale.

What You Will Do

• 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.

Qualifications

• 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.

Requirements

• 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.

Benefits

• Pay Range*: $168,800 – $236,300 annually.
• Comprehensive coverage for health, life, and financial wellbeing.
• Three (3) Medical plan options, two (2) Dental plan options, and a Vision plan.
• Voluntary Critical Illness, Hospital Indemnity, and Accident plans.
• Pre-tax savings options including a partially employer funded Health Savings Account and employee Flexible Savings Accounts.
• 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.
• Supportlinc for mental health coaching and resources.
• LifeBalance for year-round discounts on various services.
• Flexible paid vacation, company-paid holidays, paid sick time, paid parental leave, identity theft program, travel assistance, and fitness discounts.