Senior Associate Decision Scientist

Farmers Insurance

33d•$85,800 - $163,125

About The Position

The Senior Associate Decision Scientist helps transform complex business questions into actionable analytical solutions that shape strategy across Farmers’ Direct & Strategic Partnerships organization. The role uses advanced analytics, statistical modeling, and data visualization to uncover insights, identify opportunities, and influence business decisions. It partners with business leaders and cross-functional teams, using tools such as Python, SQL, Excel, and Power BI to analyze large datasets, develop models, and recommend actions that drive growth, improve customer outcomes, and optimize performance. The position combines technical expertise, intellectual curiosity, and business acumen. The successful candidate will solve ambiguous problems, communicate analytical findings through effective storytelling, and turn insights into measurable business impact. During the first year, the role is expected to build strong partnerships with business leaders; lead high-impact analytical projects from problem definition through implementation; develop, validate, and deploy analytical models; and deliver executive-ready insights and recommendations that advance business objectives.

Requirements

Bachelor’s degree in Data Science, Statistics, Mathematics, Economics, Business Analytics, Computer Science, or a related quantitative discipline; an equivalent combination of education and experience may be considered.
Three or more years of experience applying advanced analytics, statistical modeling, predictive analytics, or quantitative problem-solving to support business decision-making.
Demonstrated ability to translate complex or ambiguous business questions into analytical approaches, actionable insights, and measurable business outcomes.
Strong technical proficiency with Python, SQL, Excel, Power BI, and other tools for data exploration, statistical analysis, modeling, and visualization.
Experience developing, validating, and deploying analytical models, including forecasting, segmentation, driver analysis, optimization, predictive modeling, and performance measurement.
Solid foundation in statistical methods and analytical techniques, including hypothesis testing, regression analysis, experimental design, forecasting, and trend analysis.
Ability to communicate complex analytical findings to technical and non-technical audiences and translate data into recommendations that influence strategy and decisions.

Nice To Haves

Experience in insurance, financial services, or another data-driven industry is preferred.

Responsibilities

Apply moderately advanced statistical, mathematical, and analytical techniques to solve medium-to-large-scale business problems affecting current and future strategies.
Build descriptive and explanatory models or leverage outputs from predictive and machine-learning models developed by others.
Analyze large and complex datasets to identify trends, patterns, risks, and business opportunities.
Use data visualization tools and analytical methods including distribution, correlation, outlier, multivariate, and time-series analyses, as well as machine-learning techniques.
Conduct exploratory data analysis, test hypotheses, and validate findings with business stakeholders.
Partner with cross-functional teams to understand business objectives and develop data-driven solutions.
Collect, integrate, and transform data from multiple sources into cohesive datasets for analysis.
Evaluate data quality and apply data governance and data hygiene practices to ensure reliability and accuracy.
Perform quality-control checks, document processes, and collaborate with peers on data validation.
Work with IT, Data Engineering, Data Science, and other technical teams to optimize data pipelines and resolve data-quality issues.
Support deployment, monitoring, and maintenance of predictive models within business workflows.
Translate complex analyses into clear, actionable insights and recommendations for business leaders.
Use storytelling and visualization techniques to make analytical findings accessible and impactful.
Stay current on emerging data science methodologies, technologies, and industry best practices.
Contribute to continuous learning and knowledge sharing.
Provide informal coaching or mentorship to less experienced team members as needed.
Lead high-impact analytical projects from problem definition through implementation and drive measurable improvements in growth, customer experience, operational performance, and profitability.
Perform other duties as assigned.