What We Do

• Complex commercial construction projects routinely wait weeks for a single quote
• Legacy carriers rely on static applications and disconnected systems
• Brokers chase carriers through calls, emails, and resubmissions
• Faster decisions
• Smarter, more accurate pricing
• Better risk outcomes for insureds who invest in safer practices

Our Investors

• Intact Private Capital https://www.intactfc.com/about-us/intact-ventures
• Spark Capital https://www.sparkcapital.com/
• Costanoa Ventures https://costanoa.vc/
• Y Combinator https://www.ycombinator.com/
• Susa Ventures https://www.susaventures.com/
• And several others

Our Team

We're a team of technologists and insurance enthusiasts, bridging the two worlds together. Check out our About https://www.shepherdinsurance.com/about page to learn more.

Job Description about the Role

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.

What You'll Do

• 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

• 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
• 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)

Benefits

🏥 Premium Healthcare 100% contribution to top-tier health, dental, and vision

🥕 Fertility benefits and family building support

🏖️ Unlimited PTO Flexibility to take the time off, recharge, and perform

🥗 Daily lunches, dinners, and snacks We work together, and enjoy meals together too

🖥️ SF, NYC, Dallas-Fort Worth, Chicago and LA Offices

📚 Professional Development Access to premium coaching, including leadership development

🏦 Competitive 401(k) Plan

🐶 Dog-friendly office Plenty of dogs to play with and make friends with in the SF office