Actuarial Data Scientist
Develops and deploys end-to-end pricing models for commercial auto insurance, building feature pipelines from messy data and monitoring performance. Requires 3+ years experience with statistical modeling (GLMs, GBDTs), Python/SQL, and actuarial concepts.
About the job
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)
Skills
Python, SQL, Glms, Gbdts, Time Series Analysis, Bayesian Methods, Feature Engineering, Actuarial Modeling, AWS, NLP
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