Senior Data Scientist, Pie Insurance

• Bachelor’s degree in a quantitative field (Data Science, Computer Science, Statistics or other related fields) is required. A M.S. in a quantitative field is preferred
• 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

Desirable

• Working collaboratively with our Product, data engineering, and MLOps teams, you will be actively involved in the entire Model development lifecycle from conceptualization to deployment
• Enhance and reinvent the next generation of risk (frequency, severity, LR) and pricing models, with strong emphasis on model robustness
• Working with business partners, 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