FBS - Decision Scientist

Farmers Insurance is one of the United States’ largest insurers, providing a wide range of insurance and financial services products with gross written premiums well over US$25 Billion (P&C). They proudly serve more than 10 million U.S. households with more than 19 million individual policies across all 50 states through the efforts of over 48,000 exclusive and independent agents and nearly 18,500 employees. Finally, Farmers Insurance is part of one the largest Insurance Groups in the world.

About the Role

The Decision Scientist will provide analytical and statistical support to business teams by transforming complex data into meaningful insights and actionable recommendations.

In this role, you will apply statistical methodologies, quantitative techniques, and analytical models to investigate business challenges, identify trends, test hypotheses, and uncover opportunities to improve business performance, efficiency, growth, and profitability.

You will work with large and diverse datasets, conduct exploratory data analysis, develop descriptive and explanatory models, and collaborate with cross-functional teams to support data-driven decision-making.

The ideal candidate combines strong analytical and statistical knowledge with programming skills, business curiosity, and the ability to communicate complex findings to non-technical stakeholders.

What You'll Do

• Apply statistical, mathematical, and quantitative techniques to solve business problems and support strategic decision-making.
• Conduct exploratory data analysis (EDA) to identify patterns, trends, correlations, outliers, and business opportunities.
• Develop descriptive and explanatory models to investigate business challenges and support data-driven recommendations.
• Apply statistical methodologies such as distribution analysis, correlation analysis, multivariate analysis, time series, hypothesis testing, and machine learning.
• Collect, integrate, clean, and transform data from multiple sources to create reliable datasets for analysis.
• Test hypotheses, validate findings, and evaluate potential business opportunities through analytical studies and experiments.
• Interpret complex datasets and translate analytical findings into clear business insights and recommendations.
• Collaborate with business stakeholders to understand challenges, define analytical approaches, and support complex business objectives.
• Use data visualization tools to communicate trends, findings, and recommendations effectively.
• Support the deployment, monitoring, and evaluation of predictive models developed by the team.
• Perform data quality checks and apply data validation techniques to ensure accuracy, consistency, and reliability.
• Partner with data scientists, data engineers, IT, and other technical teams to improve data integrity and analytical processes.
• Identify data quality issues and collaborate with relevant teams to investigate and resolve them.
• Prepare presentations and communicate analytical results to business partners and leadership, making technical concepts accessible to non-technical audiences.
• Apply data storytelling techniques to connect analytical findings with business objectives and recommended actions.
• Stay current with emerging statistical methodologies, data science technologies, machine learning techniques, and industry best practices.
• Share knowledge and support less experienced team members when appropriate, contributing to the overall quality of the team's work.