Actuarial Data Scientist

Berkshire Hathaway GUARD Insurance Companies•Greenfield, WI

2d•$120,000 - $190,000•Hybrid

About The Position

Good Things Start Here.Good things are happening at Berkshire Hathaway GUARD Insurance Companies—an A+ (Superior) rated, nationwide Property & Casualty insurer backed by Berkshire Hathaway. With supportive leadership, collaborative teams, and opportunities to grow, GUARD is a place where people build meaningful, long‑term careers. Good Things You Can Count On. Hybrid schedule: 2 days remote / 3 in‑office Competitive pay + generous PTO Medical, dental & vision starting day one 401(k), tuition reimbursement & longevity bonuses We're seeking an Actuarial Data Scientist that would be reporting to the AVP, Data Science & Commercial Lines Pricing. This role combines actuarial analytics, predictive modeling, and technical leadership to advance pricing sophistication, risk segmentation, underwriting performance, and profitability. The successful candidate will help modernize actuarial modeling practices, develop scalable analytical solutions, and promote the adoption of data science and software engineering best practices throughout the organization.

Requirements

Bachelor's degree in Actuarial Science, Statistics, Mathematics, Data Science, Computer Science, Engineering, Economics, or a related quantitative field.
3-5 years of experience in Actuarial Data Science field
Experience developing predictive models and advanced analytical solutions in a business environment.
Strong proficiency in Python, including development of production-quality analytical code.
Advanced SQL skills for large-scale data extraction, transformation, and analysis.
Experience working with large, complex datasets and statistical modeling techniques.
Strong communication, collaboration, problem-solving, and stakeholder management skills.
Ability to work independently in a fast-paced environment.
Applicants must be authorized to work in the U.S. without current or future sponsorship

Nice To Haves

Experience working with actuarial pricing methodologies.
Experience with Commercial Lines products such as Workers Compensation, Commercial Auto, General Liability, Businessowners (BOP), Professional Liability, Umbrella, or similar coverages.
Experience leading actuarial or analytical modernization initiatives.
Familiarity with Git or Azure DevOps, code review processes, CI/CD concepts, package management, and collaborative development workflows.
Familiarity with model governance, validation, and monitoring frameworks.
Candidates with combined actuarial and data science backgrounds are strongly encouraged to apply.

Responsibilities

Partner with actuarial to develop analytical solutions that improve pricing sophistication, risk segmentation, underwriting performance, and profitability.
Develop and enhance pricing, segmentation, profitability, and risk selection models across Commercial Lines products using both actuarial methodologies and modern machine learning techniques.
Apply advanced analytics to pricing, underwriting, claims, and other insurance datasets to identify trends, emerging risks, and opportunities for profitable growth.
Support rate reviews, indication analyses, profitability studies, portfolio management, and other pricing initiatives through advanced analytical techniques.
Develop predictive models using GLMs and other statistical and machine learning approaches while balancing predictive performance, business value, interpretability, and regulatory considerations.
Research and evaluate internal and external data sources to enhance underwriting, pricing, and portfolio insights.
Build scalable, production-ready analytical workflows and collaborate with data engineering and technology teams to operationalize models and analytical solutions.
Promote best practices in model development, coding standards, testing, documentation, version control, reproducibility, model governance, and performance monitoring.
Provide technical guidance and mentorship to actuarial and analytical teams, helping advance the adoption of modern data science and software development practices.
Communicate analytical findings and recommendations to technical and non-technical audiences, including senior leadership, and help drive adoption of analytical solutions across the organization.