Senior Data Scientist

PORCH 💚

. Contribute to modeling standards and best practices for insurance pricing and risk modeling .

Posted 11/6/2026full-timeRemote • Washington • United StatesSenior💰 $138,800 - $194,300 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building and deploying GLM-based pricing models and machine learning models for insurance applications, with a strong focus on regulatory compliance and risk modeling. Proven ability to mentor junior data scientists and collaborate with cross-functional teams to integrate data science solutions into operational workflows.

Highest-signal resume keywords

GLM-Based Pricing ModelsMachine Learning ModelsPython ProficiencyMLOps ExperienceCloud-Based Data Platforms

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Data ScienceRisk ModelingStatistical AnalysisGradient Boosting ModelsExperimental DesignA/B TestingCausal AnalysisGeospatial Data AnalysisPredictive ModelingModel Governance

Soft Skills

MentoringCollaborationKnowledge SharingTechnical Guidance

Tools & Technologies

Scikit-learnStatsmodelsXgboostLightgbmBigQueryGCPConfluenceJira

Certifications & Qualifications

Master's DegreePhD

Industry Keywords

Insurance PricingActuarial ScienceCustomer Lifetime ValueRetention ModelingConversion ModelingRegulatory ComplianceGenerative AILLMs

Tech Stack

Tools & technologies

BigQueryCloudGoogle Cloud PlatformPythonScikit-LearnSQL

About the role

Key responsibilities & impact

Contribute to modeling standards and best practices for insurance pricing and risk modeling
Support and mentor junior data scientists through code review, knowledge sharing, and technical guidance
Build, validate, and deploy GLM-based frequency, severity, and loss-cost models for homeowners insurance pricing
Build machine learning models, including GBMs and neural networks, for underwriting accuracy, competitive positioning, and profitability
Develop ensemble models for insured-level profitability, customer retention, conversion, and customer lifetime value
Incorporate aerial imagery, satellite data, government records, and building permits to quantify localized risk and inform pricing and underwriting
Partner with product, actuarial, engineering, and business stakeholders to integrate data science solutions into operational workflows
Develop, file, implement, and monitor predictive models that meet regulatory requirements
Apply deployment practices including A/B testing, performance monitoring, and continuous refinement in high-traffic environments
Evaluate generative AI and LLM techniques for competitive advantage

Requirements

What you’ll need

5+ years of experience in data science, with significant depth in insurance pricing or risk modeling
Technical ownership of complex projects end-to-end and experience supporting or mentoring less-experienced data scientists
Experience building, validating, and deploying GLM-based pricing models in production, ideally for homeowners or other property/casualty lines
Experience with machine learning models for non-pricing use cases
Proficiency in Python and SQL
Experience implementing GLMs and gradient boosting models using scikit-learn, statsmodels, xgboost, and lightgbm
Experience with experimental design, model deployment, A/B testing, and causal analysis
Experience with cloud-based data platforms such as BigQuery and GCP
MLOps experience with model training pipelines, versioning, and monitoring
Proficiency with Confluence and Jira
Experience working in an Agile/Scrum environment
Master's or PhD in Statistics, Mathematics, Computer Science, or a related quantitative field preferred
Background in actuarial science or insurance mathematics preferred
Experience with customer lifetime value, retention, or conversion modeling preferred
Experience with geospatial or spatial data analysis preferred
Exposure to generative AI and LLMs, including prompt engineering or fine-tuning, preferred
Exposure to experimentation frameworks and causal inference methods preferred
Experience with model governance, validation frameworks, and regulatory compliance in insurance preferred

Benefits

Comp & perks

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
Healthcare, dependent care, and transportation Flexible Spending 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
Employer-paid Supportlinc wellbeing program, including 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-paid holidays per year
Paid sick time
Paid parental leave
Identity theft program
Travel assistance
Fitness and other discount programs