Actuarial Data Scientist

Actuarial Data Scientist

Shepherd is building the data infrastructure and predictive models that power modern commercial insurance. As an Actuarial Data Scientist 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.

About the Role

• 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)