Shepherd Actuarial Data Science Lead at Shepherd owning development of pricing models for commercial auto insurance. Combine statistical rigor with product delivery to improve pricing and product quality.
Responsibilities
Shepherd 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.
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
Qualification
Strong foundation in statisticsProficiency in Python and SQLACAS/FCAS actuarial designationAI-native mindsetExperience in insuranceExposure to telematics pricing modelsShepherd runs on four valuesThink bigWin togetherCross the aisle
Required
7+ 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
ACAS/FCAS actuarial designation
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
Experience managing a small team or project
Preferred
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)
Shepherd runs on four values. Here's what each one means in this seat.
Think big, build big. We exist to protect progress and the industries that rely on it. The work here is aimed at a system that runs on its own, and the roadmap gets sequenced backward from that rather than forward from what's easy.
Win together. We rise as one. We support each other, raise the bar, and celebrate collective success. Come set a standard the rest of the team inherits.
Cross the aisle. Collaboration wins. We listen deeply, work across boundaries, and prioritize shared success over individual lanes. The best product calls here come from engineers who've sat with underwriters and underwriters who understand where the model breaks, and much of this job is listening closely enough on both sides to make that happen.
