Actuarial Data Scientist at Shepherd

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

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

Why this role is remarkable

• 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.

What You Will Do

• 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.

The ideal candidate

• 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.