Actuarial Data Scientist

What You'll Do

• Own commercial auto pricing models end-to-end from feature development through deployment and iterate on them as the book grows and new data sources come online
• Build and deploy predictive models build and deploy loss cost models that set pricing for Shepherd's commercial auto book
• Design and maintain feature pipelines that transform raw submission, claims, and third-party data into model-ready inputs
• Collaborate with actuaries and underwriters to translate domain expertise into model features and validate outputs against real-world outcomes
• Develop model monitoring frameworks to track drift, performance degradation, and calibration over time
• Run experiments and back-tests to quantify model impact on loss ratios, pricing accuracy, and portfolio quality
• Communicate findings clearly to technical and non-technical stakeholders through concise documentation and presentations

What We're Looking For

Must-Haves

• 3+ years of professional experience building and deploying personal auto or commercial lines predictive pricing models in production
• Familiarity with actuarial concepts (loss development, exposure rating, credibility)
• Strong foundation in statistics: GLMs, GBDTs, time series analysis, heavy tail distributions, and Bayesian methods
• Proficiency in Python and SQL
• Experience with feature engineering on messy, real-world, small data
• Ability to reason from first principles and communicate results crisply to non-technical audiences
• AI-native mindset: you already use LLMs and AI tools to accelerate your own work

Nice-to-Haves

• Experience in insurance, insurtech, fintech, or other regulated industries
• Exposure to telematics pricing models
• Experience with NLP/document extraction from unstructured insurance submissions
• Prior work with model deployment infrastructure (AWS)