Autonomous Auto Pricing Data Scientist

Jack & Jill; San Francisco (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

Shepherd is seeking an Actuarial Data Scientist to own end-to-end commercial auto pricing models in a high-impact AI-native underwriting environment. You will build and deploy loss cost models and feature pipelines that drive underwriting quality and market expansion.

Join a team that blends statistical rigor with real product delivery, partnering with actuaries and underwriters to translate domain knowledge into predictive features, improving pricing accuracy and loss ratios.

Qualifications

• 3 years of professional experience building and deploying predictive pricing models for 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.

Responsibilities

• Own commercial auto pricing models end-to-end, from feature development to production deployment.
• Design and maintain feature pipelines transforming submission, claims, and third-party data into model inputs.
• Collaborate with actuaries and underwriters to translate domain expertise into predictive features that improve pricing accuracy.

Skills

Python

SQL

GLMs

GBDTs

Bayesian methods

Pricing models

Job description

Shepherd is seeking an Actuarial Data Scientist to own end-to-end commercial auto pricing models in a high-impact AI-native underwriting environment. You will build and deploy loss cost models and feature pipelines that drive underwriting quality and market expansion.

Join a team that blends statistical rigor with real product delivery, partnering with actuaries and underwriters to translate domain knowledge into predictive features, improving pricing accuracy and loss ratios.