Actuarial Data Science LeadShepherd is building the data infrastructure and predictive models that power modern commercial insurance. As an Actuarial Data Science Lead on the Actuarial & Predictive Analytics team, you will own the development of pricing models starting with commercial auto, one of our highest-volume and most data-rich lines. You'll directly shape the quality of the book we write and the products we bring to market.This is a high-impact, individual-contributor role for someone who thrives at the intersection of statistical rigor and shipping real products.
You will work closely with actuaries, underwriters, and engineers to turn data into decisions.What You'll DoOwn 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 onlineBuild and deploy predictive models build and deploy loss cost models that set pricing for Shepherd's commercial auto bookDesign and maintain feature pipelines that transform raw submission, claims, and third-party data into model-ready inputsCollaborate with actuaries and underwriters to translate domain expertise into model features and validate outputs against real-world outcomesDevelop model monitoring frameworks to track drift, performance degradation, and calibration over timeRun experiments and back-tests to quantify model impact on loss ratios, pricing accuracy, and portfolio qualityCommunicate findings clearly to technical and non-technical stakeholders through concise documentation and presentationsWhat We're Looking ForMust-Haves7+ years of professional experience building and deploying personal auto or commercial lines predictive pricing models in productionFamiliarity with actuarial concepts (loss development, exposure rating, credibility)Strong foundation in statistics: GLMs, GBDTs, time series analysis, heavy tail distributions, and Bayesian methodsProficiency in Python and SQLACAS/FCAS actuarial designationExperience with feature engineering on messy, real-world, small dataAbility to reason from first principles and communicate results crisply to non-technical audiencesAI-native mindset: you already use LLMs and AI tools to accelerate your own workExperience managing a small team or projectNice-to-HavesExperience in insurance, insurtech, fintech, or other regulated industriesExposure to telematics pricing modelsExperience with NLP/document extraction from unstructured insurance submissionsPrior work with model deployment infrastructure (AWS)