P&C Data Analyst: Pricing & Insights (Onsite)

Farm Family; Richmond (VA); On-site; USD 68,000 - 98,000; Full time

Job summary

Farm Family is seeking an onsite P&C Data Analyst to develop and improve analytical models for pricing, underwriting, and portfolio management. You will work with pricing actuaries, underwriting, claims, distribution, and IT to translate questions into data-driven solutions.

The role emphasizes data quality, model governance, and collaboration across teams to deliver actionable insights and monitoring of performance over time.

Qualifications

• Bachelor’s degree in mathematics, statistics, actuarial science, computer science, data science, or a related quantitative field
• Two to four years of relevant experience in data science, analytics, actuarial support, or related quantitative roles
• A master’s degree may be considered in lieu of one to two years of experience
• Proficiency in analytical programming languages and tools (e.g., SQL, Python, R)
• Experience working with large, complex datasets
• Strong analytical thinking, problem-solving, and communication skills
• Strong diligence and commitment to data quality

Responsibilities

• Support pricing analyses, rate reviews, segmentation evaluations, portfolio monitoring, and performance assessments across Personal and Commercial Lines products
• Analyze trends in frequency, severity, retention, growth, profitability, and other key insurance performance metrics
• Partner with actuarial, underwriting, and product teams to identify business challenges and develop data-driven recommendations
• Assist in evaluating the impact of underwriting, pricing, and product changes on business performance
• Develop recurring and ad hoc analyses that support strategic and operational decision-making
• Work with large, structured and semi-structured datasets using SQL and analytical programming languages (e.g., Python or R)
• Ensure data quality, reproducibility, and proper documentation of models, assumptions, and results
• Support adherence to model development best practices and model risk governance standards
• Support data science and predictive modeling initiatives through data preparation, feature development, exploratory analysis, and model performance reporting
• Collaborate with data scientists and actuarial partners on analytical projects involving risk segmentation, predictive modeling, and portfolio optimization
• Develop foundational skills in statistical analysis, predictive modeling, and analytical programming
• Participate in testing, monitoring, and implementation of analytical models and business rules
• Contribute to the evaluation of new data sources and analytical techniques that improve pricing and underwriting decisions
• Collaborate with actuarial, underwriting, distribution, and product stakeholders to define analytical questions and success criteria
• Translate technical analyses into clear, actionable insights using appropriate visualizations and written summaries
• Respond to feedback and iterate on analyses to better support business decisions
• Partner with technical teams to support implementation of analytical and reporting solutions
• Identify opportunities to improve reporting processes, data quality, analytical efficiency, and business insights
• Stay current on industry trends, pricing methodologies, and analytical best practices within P&C insurance
• Contribute ideas for improving models, processes, and data usage within the pricing analytics function

Skills

Analytical thinking

Communication

Education

Bachelor's degree in quantitative field

Master's degree may substitute 1–2 years experience

Tools

SQL

Python

R

Job description

Farm Family is seeking an onsite P&C Data Analyst to develop and improve analytical models for pricing, underwriting, and portfolio management. You will work with pricing actuaries, underwriting, claims, distribution, and IT to translate questions into data-driven solutions.

The role emphasizes data quality, model governance, and collaboration across teams to deliver actionable insights and monitoring of performance over time.