Shepherd→
Actuarial Data Scientist at Shepherd in San Francisco, CA
Skills
Job Description
What We Do
Shepherd is an AI-native commercial insurance platform transforming how high-hazard industries get covered. Our mission is to make risk frictionless for the builders and operators shaping the physical world — protecting progress from concept through construction and into decades of operation.
The infrastructure behind the AI boom — data centers, semiconductor fabs, renewable energy assets — has to be built and insured. But traditional carriers weren't built for this speed:
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
We built Shepherd to solve that. Our AI performs the same underwriting workflows in seconds, and integrates real-time data from construction technology partners — Procore, Autodesk, OpenSpace, DroneDeploy, and others — to see risk as it actually exists, not just as it was reported on a static form.
We're pursuing the most ambitious technical vision in commercial insurance: fully autonomous underwriting. We're closing in on the first fully agentic submission in the industry — email in, price out, no human intervention until the last mile.
With Shepherd, safety, speed, and quality no longer trade off against one another — they compound. We're building:
Faster decisions
Smarter, more accurate pricing
Better risk outcomes for insureds who invest in safer practices
We're not just modernizing insurance products. We're building the risk infrastructure for the next generation of financial services.
Our Investors
In March 2026, Shepherd raised a $42M Series B — bringing total funding to over $60M — led by Intact Private Capital, the investment arm of one of the largest insurers in the world. Intact is not only our lead investor but also a carrier partner, a testament to the confidence the incumbent industry has in what we're building. Our investors:
Our Team
We're a team of technologists and insurance enthusiasts, bridging the two worlds together. Check out our 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
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)
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