Senior Data Scientist
Insurance Modeling
Confirmed live in the last 24 hours
Pie Insurance

201-500 employees

Online platform simplifying small business insurance
Company Overview
Pie Insurance is a leading company in the small business insurance sector, leveraging seasoned expertise in technology and insurance to offer cost-effective, simplified, and transparent solutions. Their competitive edge lies in their efficient online platform, which allows business owners to receive a quote within just 3 minutes, significantly reducing time and effort. This customer-centric approach, combined with their commitment to transparency, positions Pie Insurance as a strong industry leader.
Financial Services
Data & Analytics

Company Stage

Series D

Total Funding





Washington, District of Columbia

Growth & Insights

6 month growth


1 year growth


2 year growth

Remote in USA
Experience Level
Desired Skills
Data Science
Data Science
Data & Analytics
  • Bachelor’s degree in a quantitative field (Data Science, Computer Science, Statistics or other related fields)
  • 5+ years experience as a data scientist, or actuarial modeler, building and delivering pricing and risk modeling solutions in the P&C insurance space
  • 3+ years experience building claims models
  • Experience developing territorial risk models
  • Strong experience in writing complex SQL programming/queries
  • Strong Python / R programming experience
  • Track record of delivering robust solutions and eager to learn new lines of business
  • Strong problem-solving and analytical skills
  • Ability to work in a fast-paced, agile environment and handle multiple projects simultaneously
  • Establish Pie as the preeminent commercial insurance among small business owners
  • Conceptualize, design, generate and test hypotheses, construct features, build and validate various pricing, underwriting, and claims models
  • Develop better predictors based on tabular and text data from internal and external sources
  • Enhance and reinvent the next generation of risk and pricing models
  • Design and build AI-ML solutions in claims, underwriting (UW), customer behaviors use-cases
  • Build demand elasticity models, assess the impact on key business metrics of rate changes for different subpopulations, and make recommendations on path forward
  • Conduct post-hoc model diagnostics and build interpretability reasons using ML methods
  • Monitor and evaluate the performance of various models; detect and come up with mitigation strategies for addressing performance degradation
  • Leverage experimentation techniques to construct the best overlays for relevant risk models
  • Monitor relevant KPIs, and develop automation process for revising risk overlays
  • Build new high-signal insightful features, analyzing a diverse set of internal and external data, and leveraging leverage deep learning, NLP, and advanced ML
  • Support the MLOps in deployment and testing of machine learning models and specialized AI models into the operations of the organization
  • Build and maintain scalable ML development pipelines to support automation and reusability
  • Showcase AI-ML capabilities to leadership and peers