Actuarial Data Science Lead
New York City · San Francisco · Chicago · NYC, New York, United States
- Pay
- Pay not listedSimilar roles state $153K–$225K
- Work setting
On-site — work setting source
Location type: On-site
From the employer’s posting- Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting- Team
Actuarial — team source
Department: Actuarial
From the employer’s posting
What you’ll work on
Full postingShepherd is building the data infrastructure and predictive models that power modern commercial insurance.
This is a high-impact, individual-contributor role for someone who thrives at the intersection of statistical rigor and shipping real products.
Own commercial auto pricing models from feature development through deployment
Build and deploy loss cost models that set pricing for the commercial auto book
Design and maintain feature pipelines from submission, claims, and third-party data
Develop model monitoring for drift, performance, and calibration
Run experiments and back-tests on loss ratios and pricing accuracy
From the employer’s posting
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.
Own commercial auto pricing models end-to-end from feature development through deployment
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
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
Tools in this posting
- Python
- SQL
- AWS
Source — Tool mentions in context
- 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 NLP/document extraction from unstructured insurance submissions - Prior work with model deployment infrastructure (AWS) How we work
Benefits in the posting
Full benefits wording- 🏥 Premium Healthcare 100% contribution to health, dental, and vision
- 🥕 Fertility benefits and family building support
- 🏖️ Flexible PTO Flexibility to take the time off, recharge, and perform
- 🏦 Competitive 401(k) Plan
From the employer’s posting.
Posting record
Report an errorOpen 4 monthsWe first saw this posting 149 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Jun 2, 2026 · 149 days ago
- Recorded sightings
- 96
- Last seen by us
- Oct 30, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Your next step
- Have your resume and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the pay range before committing time to the process.
Complete your application on jobs.ashbyhq.com. The employer’s form will show what is required.
Already applied? Track this application
Job description
Build the insurance behind the physical world.
The world is building data centers, energy projects, hospitals, schools, and critical infrastructure. Every one of those projects needs insurance to move forward. Yet the process of getting that insurance still depends on scattered documents, manual work, and weeks of back-and-forth.
Shepherd is rebuilding commercial insurance with software, data, AI, and deep underwriting expertise. We started in construction and energy, where understanding the specifics of a project matters and better decisions have consequences far beyond a screen.
We develop insurance products, decide which risks to insure and how to price them, and partner with established insurers to back the coverage. We own the platform our team uses to turn complex submissions into coverage for the builders and operators shaping the world around us.
Real problems. Real ownership.
Our engineers, designers, underwriters, CX, and actuaries build together. The problems span turning messy documents into reliable data, making pricing more precise, and designing AI agents that can take on increasingly complex underwriting work.
The ambition is a platform where experts spend more time on judgment and relationships, supported by software that handles the work around them. Join us to build systems whose decisions help real projects get built.
630+ customers / $500B+ insured project value
Capital to build. Partners to grow with.
$67M total funding / $42M Series B in March 2026
Shepherd is backed by Intact Private Capital, Costanoa Ventures, Spark Capital, Susa Ventures, and Y Combinator. In March 2026, we announced a $42M Series B led by Intact Private Capital, bringing our total funding to $67M.
Intact is also a capacity partner. Alongside its investment, Intact works with Shepherd to bring insurance products to market. That relationship connects our platform and underwriting expertise with the resources of a global insurer.
A bigger opportunity ahead
We are investing in new insurance products, our underwriting team, and the path toward autonomous underwriting. For the people joining Shepherd, that means the chance to shape both the technology and the business it makes possible.
Our backing brings together investors who build technology companies and partners who understand insurance. We are building a company that earns the confidence of both.
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
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)
Shepherd runs on four values. Here's what each one means in this seat.
Think big, build big. We exist to protect progress and the industries that rely on it. The work here is aimed at a system that runs on its own, and the roadmap gets sequenced backward from that rather than forward from what's easy.
Win together. We rise as one. We support each other, raise the bar, and celebrate collective success. Come set a standard the rest of the team inherits.
Cross the aisle. Collaboration wins. We listen deeply, work across boundaries, and prioritize shared success over individual lanes. The best product calls here come from engineers who've sat with underwriters and underwriters who understand where the model breaks, and much of this job is listening closely enough on both sides to make that happen.
Go get it. We act with urgency, move with confidence, take smart risks, and push forward with intention. Nobody hands you the roadmap, the data, or the meeting invite. You pull the failing runs, book the time with the underwriters, and decide what matters.
🏥 Premium Healthcare
100% contribution to health, dental, and vision
🥕 Fertility benefits and family building support
🏖️ Flexible 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
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
NYC, New York, United States
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
See how this role fits your experience
Add your resume to compare the role’s scope, tools and requirements with your experience.