ACTUARIAL DATA SCIENCE LEAD

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.

Responsibilities

• 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

Requirements

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
• Able 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
• Prior work with model deployment infrastructure (AWS)

Qualifications

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
• Able 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

Skills

• Python
• SQL
• GLMs
• GBDTs
• Time series analysis
• Heavy tail distributions
• Bayesian methods
• Feature engineering
• Model monitoring
• Experimentation and back-testing
• Communication

Benefits

• Premium Healthcare (100% contribution to top-tier health, dental, and vision)
• Fertility benefits and family building support
• Unlimited PTO
• Daily lunches, dinners, and snacks
• 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 (SF office only)

Pay

$200K - $240K

Schedule

Not specified