Actuarial Data Science Lead

Shepherd

San Francisco (CA)

On-site

USD 120,000 - 160,000

Full time

14 days+

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Benefits offered by this job

Premium Healthcare

Unlimited PTO

Daily meals provided

Professional Development

Dog-friendly office

Job summary

Shepherd in San Francisco is seeking an Actuarial Data Science Lead to develop pricing models for commercial auto insurance. This role involves collaborating with actuaries and engineers to use data for informed decisions and improvements on pricing accuracy.

The ideal candidate will have over 7 years of experience in predictive pricing and a strong background in statistics, particularly in the insurance domain. Benefits include premium healthcare, unlimited PTO, and a dog-friendly office environment.

Qualifications

7+ years of experience with commercial lines predictive pricing models.
Strong foundation in statistics including GLMs and Bayesian methods.
Ability to communicate complex results to non-technical audiences.

Responsibilities

Own commercial auto pricing models end-to-end and iterate as needed.
Build and deploy loss cost models for pricing decisions.
Collaborate to translate domain expertise into tangible model features.

Skills

Predictive pricing models

Statistics

Python

SQL

Actuarial concepts

Education

ACAS/FCAS actuarial designation

Tools

AWS

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:

Intact Private Capital
Spark Capital
Costanoa Ventures
Y Combinator
Susa Ventures
And several others

Our Team

We're a team of technologists and insurance enthusiasts, bridging the two worlds together. Check out our About page to learn more.

About the Role

Shepherd is building the data infrastructure and predictive models that power modern commercial insurance. As an Actuarial Data Science Lead 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

7+ 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
ACAS/FCAS actuarial designation
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 managing a small team or project

Nice‑to‑Haves