Actuarial Data Scientist at Shepherd

Actuarial Data Scientist

Salary Not Disclosed

Company Description Shepherd is a $60M-funded, AI-native commercial insurance platform backed by Intact Private Capital, Spark Capital, and Y Combinator, transforming high-hazard industry underwriting through autonomous workflows.

Job Description You will join the Actuarial & Predictive Analytics team to own end-to-end commercial auto pricing models. By building and deploying sophisticated loss cost models and feature pipelines, you’ll directly influence Shepherd’s underwriting quality and market expansion. This high-impact role blends statistical rigor with shipping real products to achieve the industry’s first fully autonomous underwriting.

Location San Francisco, NYC, or Chicago, USA

• Work at the cutting edge of insurtech, building the industry’s first fully agentic submission system for complex commercial construction projects.
• Benefit from strong backing and industry validation, following a $42M Series B led by Intact Private Capital, one of the world’s largest insurers.
• Directly shape the risk infrastructure for the next generation of financial services, leveraging real-time data from partners like Procore and Autodesk.

• Own commercial auto pricing models end-to-end, from initial feature development through production deployment and iterative refinement.
• Design and maintain robust feature pipelines that transform raw submission, claims, and third-party data into high-quality model inputs.
• Collaborate closely with actuaries and underwriters to translate domain expertise into predictive features that improve pricing accuracy and loss ratios.

• 3 years of professional experience building and deploying predictive pricing models for personal or commercial auto insurance in production environments.
• Strong command of statistical methods including GLMs, GBDTs, and Bayesian methods, alongside proficiency in Python and SQL.
• An AI-native mindset with the ability to reason from first principles and communicate complex findings to non-technical stakeholders.